ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network

We propose an analysis of surgical videos that is based on a novel recurrent convolutional network (SV-RCNet), specifically for automatic workflow recognition from surgical videos online, which is a key component for developing the context-aware computer-assisted intervention systems. Different from previous methods which harness visual and temporal information separately, the proposed SV-RCNet seamlessly integrates a convolutional neural network (CNN) and a recurrent neural network (RNN) to forma novel recurrent convolutional architecture in order to take full advantages of the complementary information of visual and temporal features learned from surgical videos. We effectively train the SV-RCNet in an end-to-end manner so that the visual representations and sequential dynamics can be jointly optimized in the learning process. In order to produce more discriminative spatio-temporal features, we exploit a deep residual network (ResNet) and a long short term memory (LSTM) network, to extract visual features and temporal dependencies, respectively, and integrate them into the SV-RCNet. Moreover, based on the phase transition-sensitive predictions from the SV-RCNet, we propose a simple yet effective inference scheme, namely the prior knowledge inference (PKI), by leveraging the natural characteristic of surgical video. Such a strategy further improves the consistency of results and largely boosts the recognition performance. Extensive experiments have been conducted with the MICCAI 2016 Modeling and Monitoring of Computer Assisted Interventions Workflow Challenge dataset and Cholec80 dataset to validate SV-RCNet. Our approach not only achieves superior performance on these two datasets but also outperforms the state-of-the-art methods by a significant margin.
Automating unambiguous NOE data usage in NVR for NMR protein structure-based assignments

Nuclear Magnetic Resonance (NMR) Spectroscopy is an important technique that allows determining protein structure in solution. An important problem in protein structure determination using NMR spectroscopy is the mapping of peaks to corresponding amino acids, also known as the assignment problem. Structure-Based Assignment (SBA) is an approach to solve this problem using a template structure that is homologous to the target. Our previously developed approach Nuclear Vector Replacement-Binary Integer Programming (NVR-BIP) computed the optimal solution for small proteins, but was unable to solve the assignments of large proteins. NVR-Ant Colony Optimization (ACO) extended the applicability of the NVR approach for such proteins. One of the input data utilized in these approaches is the Nuclear Overhauser Effect (NOE) data. NOE is an interaction observed between two protons if the protons are located close in space. These protons could be amide protons, protons attached to the alpha-carbon atom in the backbone of the protein, or side chain protons. NVR only uses backbone protons. In this paper, we reformulate the NVR-BIP model to distinguish the type of proton in NOE data and use the corresponding proton coordinates in the extended formulation. In addition, the threshold value over interproton distances is set in a standard manner for all proteins by extracting the NOE upper bound distance information from the data. We also convert NOE intensities into distance thresholds. Our new approach thus handles the NOE data correctly and without manually determined parameters. We accordingly adapt NVR-ACO solution methodology to these changes. Computational results show that our approaches obtain optimal solutions for small proteins. For the large proteins our ant colony optimization-based approach obtains promising results.
Expert systems in production planning and scheduling: A state-of-the-art survey

Intelligent solutions, based on expert systems, to solve problems in the field of production planning and scheduling are becoming more and more widespread nowadays. Especially the last decade has witnessed a growing number of manufacturing companies, including glass, oil, aerospace, computers, electronics, metal and chemical industries-to name just a few-interested in the applications of expert systems (ESs) in manufacturing. This paper is a state-of-the-art review of the use of ESs in the field of production planning and scheduling. The paper presents famous expert systems known in the literature and current applications, analyzes the relative benefits and concludes by sharing thoughts and estimations on ESs future prospects in this area.
Temporal Readout Noise Analysis and Reduction Techniques for Low-Light CMOS Image Sensors

In this paper, an analytical noise calculation is presented to derive the impact of process and design parameters on 1/f and thermal noise for a low-noise CMOS image sensor (CIS) readout chain. It is shown that dramatic noise reduction is obtained by using a thin-oxide transistor as the source follower of a typical 4T pixel. This approach is confirmed by a test chip designed in a 180-nm CIS process and embedding small arrays of the proposed new pixels together with state-ofthe- art 4T pixels for comparison. The new pixels feature a pitch of 7.5 mu m and a fill factor of 66%. A 0.4e-rms input-referred noise and a 185-mu V/e-conversion gain are obtained. Compared with state-of-the-art pixels, also present onto the test chip, the rms noise is divided by more than 2 and the conversion gain is multiplied by 2.2.
A spatiotemporal reconstruction of the C. elegans pharyngeal cuticle reveals a structure rich in phase-separating proteins

How the cuticles of the roughly 4.5 million species of ecdysozoan animals are constructed is not well understood. Here, we systematically mine gene expression datasets to uncover the spatiotemporal blueprint for how the chitin-based pharyngeal cuticle of the nematode Caenorhabditis elegans is built. We demonstrate that the blueprint correctly predicts expression patterns and functional relevance to cuticle development. We find that as larvae prepare to molt, catabolic enzymes are upregulated and the genes that encode chitin synthase, chitin cross-linkers, and homologs of amyloid regulators subsequently peak in expression. Forty-eight percent of the gene products secreted during the molt are predicted to be intrinsically disordered proteins (IDPs), many of which belong to four distinct families whose transcripts are expressed in overlapping waves. These include the IDPAs, IDPBs, and IDPCs, which are introduced for the first time here. All four families have sequence properties that drive phase separation and we demonstrate phase separation for one exemplar in vitro. This systematic analysis represents the first blueprint for cuticle construction and highlights the massive contribution that phase-separating materials make to the structure.
Proteomic and metabonomic analysis uncovering Enterovirus A71 reprogramming host cell metabolic pathway

Enterovirus A71 (EV71) infection can cause hand, foot, and mouth disease (HFMD) and severe neurological complications in children. However, the biological processes regulated by EV71 remain poorly understood. Herein, proteomics and metabonomics studies were conducted to uncover the mechanism of EV71 infection in rhabdomyosarcoma (RD) cells and identify potential drug targets. Differential expressed proteins from enriched membrane were analyzed by isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics technology. Twenty-six differential proteins with 1.5-fold (p < 0.05) change were detected, including 14 upregulated proteins and 12 downregulated proteins. The upregulated proteins are mainly involved in metabolic process, especially in the glycolysis pathway. Alpha-enolase (ENO1) protein was found to increase with temporal dependence following EV71 infection. The targeted metabolomics analysis revealed that glucose absorption and glycolysis metabolites were increased after EV71 infection. The glycolysis pathway was inhibited by knocking down ENO1 or the use of a glycolysis inhibitor (dichloroacetic acid [DCA]); and we found that EV71 infection was inhibited by depleting ENO1 or using DCA. Our study indicates that EV71 may reprogram glucose metabolism by activating glycolysis, and EV71 infection can be inhibited by interrupting the glycolysis pathway. ENO1 may be a potential target against EV71, and DCA could act as an inhibitor of EV71.
SAR Image Despeckling Using Continuous Attention Module

Speckle removal process is inevitable in the restoration of synthetic aperture radar (SAR) images. Several variant methods have been proposed for enhancing SAR images over the past decades. However, in recent studies, convolutional neural networks (CNNs) have been widely applied in SAR image despeckling because of their versatility in representation learning. Nonetheless, a fair number of textures of the images are still lost when despeckling using simple CNN structures. To solve this problem, an encoder-decoder architecture was previously proposed. Although this architecture extracts features on different scales and has been shown to yield state-of-the-art performance, it still learns representation locally, resulting in missing overall information of convolutional features. Therefore, we herein introduce a new method for SAR image despeckling (SAR-CAM), which improves the performance of an encoder-decoder CNN architecture by using various attention modules. Moreover, a context block is introduced at the minimum scale to capture multiscale information. The model is trained via a data-driven approach using the gradient descent algorithm with a combination of modified despeckling gain and total variation loss function. Experiments performed on simulated and real SAR data demonstrate that the proposed method achieves significant improvements over state-of-the-art methodologies.
ATM- and ATR-induced primary ciliogenesis promotes cisplatin resistance in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers because of its late diagnosis and chemoresistance. Primary cilia, the cellular antennae, are observed in most human cells to maintain development and differentiation. Primary cilia are gradually lost during the progression of pancreatic cancer and are eventually absent in PDAC. Here, we showed that cisplatin-resistant PDAC regrew primary cilia. Additionally, genetic or pharmacological disruption of primary cilia sensitized PDAC to cisplatin treatment. Mechanistically, ataxia telangiectasia mutated (ATM) and ATM and RAD3-related (ATR), tumor suppressors that initiate DNA damage responses, promoted the excessive formation of centriolar satellites (EFoCS) and autophagy activation. Disruption of EFoCS and autophagy inhibited primary ciliogenesis, sensitizing PDAC cells to cisplatin treatment. Collectively, our findings revealed an unexpected interplay among the DNA damage response, primary cilia, and chemoresistance in PDAC and deciphered the molecular mechanism by which ATM/ATR-mediated EFoCS and autophagy cooperatively regulate primary ciliogenesis.
Graphitic carbon nitride nanosheets as promising candidates for the detection of hazardous contaminants of environmental and biological concern in aqueous matrices

Monitoring of pollutant and toxic substances is essential for cleaner environment and healthy life. Sensing of various environmental contaminants and biomolecules such as heavy metals, pharmaceutics, toxic gases, volatile organic compounds, food toxins, and pathogens is of high importance to guaranty the good health and sustainable environment to community. In recent years, graphitic carbon nitride (g-CN) has drawn a significant amount of interest as a sensor due to its large surface area and unique electrochemical properties, low bandgap energy, high thermal and chemical stability, facile synthesis, nontoxicity, and electron rich property. Furthermore, the binary and ternary nanocomposites of graphitic carbon nitride further enhance their performance as a sensor making it a cost effective, fast, and reliable gadget for the purpose, and opens a wide area of research. Numerous reviews addressing a variety of applications including photocatalytic energy conversion, photoelectrochemical detection, and hydrogen evolution of graphitic carbon nitride have been documented to date. But a lesser attention has been devoted to the mechanistic approaches towards sensing of variety of pollutants concerned with environmental and biological aspects. Herein, we present the sensing features of graphitic carbon nitride towards the detection of various analytes including toxic heavy metals, pharmaceuticals, phenolic compounds, nitroaromatic compounds, volatile organic molecules, toxic gases, and foodborne pathogens. This review will undoubtedly provide future insights for researchers working in the field of sensors, allowing them to investigate the intriguing graphitic carbon nitride material as a sensing platform that is comparable to several other nanomaterials documented in the literature. Therefore, we hope that this study could reveal some intriguing sensing properties of graphitic carbon nitride, which may help researchers better understand how it interacts with contaminants of environmental and biological concern. Graphitic carbon nitride Nanosheets as Promising Analytical Tool for Environmental and Biological Monitoring of Hazardous Substances.
Environmental assessment of fabric wet processing from gate-to-gate perspective: Comparative study of weaving and materials

Textile industry has yet to be developed beyond low efficiency, high resources consumption, and toxic emissions, with wet processing process a dominant contributor to resources consumption and pollution emissions. Recognition of the environmental impact of the representative wet processing is essential to achieve eco-friendly development of textile industry. Using Life Cycle Assessment (LCA), this study addressed the environmental impacts of wet processing of woven/knitted cotton and polyester fabrics from 4 textile enterprises in China by deploying gate to gate system boundary. One ton of grey cloth was chosen as the functional unit. Eighteen midpoint impact categories and three endpoint impact categories were assessed via ReCiPe 2016 v1.1 (H) method. The results indicated "dyeing unit" as the dominant unit for all the impact categories at the midpoint, which was mainly derived from electricity consumed by cotton wet processing and detergents used in polyester wet processing. Among 4 different fabric wet processing, woven polyester wet processing exhibited the highest impact, while the least impact was assigned to knitted cotton. In the midpoint categories of water use, dyeing unit was also the major contributor in wet processing of knitted cotton (41.20 m3) and knitted polyester (44.70 m3). Pretreatment accounted for an overwhelming percentage of water use in woven cotton (48.00 %) and woven polyester (56.00 %). Woven polyester wet processing was also the most energy-intensive and resource-consuming industry among all scenarios, with a 3.37-fold higher fossil resource scarcity per ton of fabric compared with woven cotton. The results recommend measures for cleaner production in the wet processing.
Permanent central diabetes insipidus after traumatic brain injury. Case report and literature review

The authors report permanent central diabetes insipidus (CDI) in a patient after severe traumatic brain injury (TBI) in traffic accident. A 16-year-old boy entered to a medical facility in coma (GCS score 6) with the following diagnosis: acute TBI, severe cerebral contusion, subarachnoid hemorrhage, depressed comminuted cranial vault fracture, basilar skull fracture, visceral contusion. CDI was diagnosed in 3 days after injury considering polyuria and hypernatremia (155 mmol/l). Desmopressin therapy was initiated through a feeding tube. Thirst appeared when a patient came out of the coma after 21 days despite ongoing desmopressin therapy. Considering persistent thirst and polyuria, we continued desmopressin therapy in a spray form. Under this therapy, polyuria reduced to 3-3.5 liters per a day. Symptoms of CDI persisted in long-term period (2 years after TBI) while function of adenohypophysis was intact. This case demonstrates a rare development of permanent diabetes insipidus after TBI. CDI manifested only as polyuria and hypernatremia in coma. Thirst joined after recovery of consciousness. Probable causes of CDI were damage to neurohypophysis and partially injury of pituitary stalk because of extended basilar skull fracture and/or irreversible secondary lesion of hypothalamus following diffuse axonal damage after TBI.
Facing the Void: Overcoming Missing Data in Multi-View Imagery

In some scenarios, a single input image may not be enough to allow the object classification. In those cases, it is crucial to explore the complementary information extracted from images presenting the same object from multiple perspectives (or views) in order to enhance the general scene understanding and, consequently, increase the performance. However, this task, commonly called multi-view image classification, has a major challenge: missing data. In this paper, we propose a novel technique for multi-view image classification robust to this problem. The proposed method, based on state-of-the-art deep learning-based approaches and metric learning, can be easily adapted and exploited in other applications and domains. A systematic evaluation of the proposed algorithm was conducted using two multi-view aerial-ground datasets with very distinct properties. Results show that the proposed algorithm provides improvements in multi-view image classification accuracy when compared to state-of-the-art methods.
Edge-based color constancy

Color constancy is the ability to measure colors of objects independent of the color of the light source. A well-known color constancy method is based on the gray-world assumption which assumes that the average reflectance of surfaces in the world is achromatic. In this paper, we propose a new hypothesis for color constancy namely the gray-edge hypothesis, which assumes that the average edge difference in a scene is achromatic. Based on this hypothesis, we propose an algorithm for color constancy. Contrary to existing color constancy algorithms, which are computed from the zero-order structure of images, our method is based on the derivative structure of images. Furthermore, we propose a framework which unifies a variety of known (gray-world, max-RGB, Minkowski norm) and the newly proposed gray-edge and higher order gray-edge algorithms. The quality of the various instantiations of the framework is tested and compared to the state-of-the-art color constancy methods on two large data sets of images recording objects under a large number of different light sources. The experiments show that the proposed color constancy algorithms obtain comparable results as the state-of-the-art color constancy methods with the merit of being computationally more efficient.
Silicon Thyristors for Ultrahigh Power (GW) Applications

Evolution of thyristor technology and the design concepts, which brought and maintain the phase control thyristor (PCT) at the top of a power pyramid, are discussed. The state-of-the-art device concepts like electrically triggered thyristor and light triggered thyristor are described for voltage classes up to 8.5 kV and maximal on-state rated current of 6 kA. Main focus is laid on the PCTs for high-voltage direct current transmission, the enabler of power transmission beyond the 10-GW level.
Context model based edge preservation filter for impulse noise removal

In this article, a new edge preserving contextual model based image restoration technique is proposed for images affected by impulse noise. The proposed restoration technique consists of two stages: noisy pixel identification and restoration. Center sliding window is considered as current processing pixel for both noisy pixel identification and restoration. In the first stage of the proposed technique, we follow an absolute directional difference of the neighborhood pixels to identify the pixels those are affected by impulse noise. We propose an edge preserving contextual model to restore the noisy pixels. The noise correction stage of the proposed scheme depends on the context model of the noise-free pixels in the selected window. The parameters of the contextual model are obtained using a Gaussian kernel. The proposed algorithm is tested on nine benchmark test images. The evaluation of the proposed algorithm is carried out by comparing it against nine competitive state-of-the-art algorithms for impulse noise removal. The proposed algorithm is evaluated using Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM), Non-shifted Edge Ratio (NSER) and Correlation Factor (CF) performance measures. Experimental results corroborate that the proposed algorithm provides better performance than the existing state-of-art impulse denoising methods. (C) 2017 Elsevier Ltd. All rights reserved.
Cultures of peace and the art of war in Saharan rock art

Based on the quality and abundance of its rock art, the Central Sahara constitutes a veritable open-air repository of extraordinary testimonies of the lives of Neolithic herders. How can we understand and interpret this rock art? Why the images? Why do the bovidian representations of daily life in the Saharan Neolithic with surprising realism give way to schematic images and cryptic imprints that express the existential uncertainties of "Round Head" societies confronted with a changing world? In distinguishing the myth, substance of the collective unconscious, from the mythological stories that it generates, the present author brings forth in successive stages the underlying logic of these astonishing compositions. Drawing on the combined methods of prehistoric archaeology, art history, and sociocultural anthropology, I illustrate the principles for an anthropology of rock art. Finally, in placing these results in the context of the prehistoric Saharan ecology, I indicate the potential causes of tension and conflict among the groups of pastoral nomads. Before aridity and desertification favored the emergence of a protohistoric society of merchants and warriors, the Bovidian groups established the conditions that allowed for the rise and maintenance of a grand pastoral civilization. The stories that captured the essence of this society are lost forever, but the mythological system that generated them remains fleetingly visible. (C) 2017 Elsevier Ltd and INQUA. All rights reserved.
Malignant peripheral nerve sheath tumor of the maxilla: Case report and review of the literature with emphasis on its poor prognosis

Malignant peripheral nerve sheath tumor (MPNST) is a spindle cell sarcoma with poor prognosis. Although patients with neurofibromatosis type 1 (NF1) have a higher risk of MPNST, it can also occur in the sporadic setting and may rarely arise centrally within bone. In this study, we present an extremely rare case of intraosseous MPNST of the maxilla arising in a 38-year-old female with no history of NF1. Despite radical surgery and postoperative radiotherapy, the patient died due to multiple distant metastases 1 year after treatment. According to the results of the literature analysis performed in this study, maxillary MPNST cases have worse clinical outcomes than general MPNSTs. In addition, it seems that NF1 and histological necrosis are poor prognostic indicators in patients with maxillary MPNST.