ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A Novel Threshold Detection Technique for the Automatic Construction of Attribute Profiles in Hyperspectral Images

Attribute profiles are well-acknowledged as one of the most significant techniques to characterize spectral-spatial properties of a hyperspectral image. The spectral-spatial content of an attribute profile is influenced by the threshold values considered during its construction. In this article, we propose a robust method to detect the threshold values automatically by overcoming the limitations of the existing techniques. The proposed method employs a tree structure representing the connected components of the image and evaluates attribute values at each node. Then, a total characteristic function (TCF) is defined that represents these attribute values in a nondecreasing order. The defined TCF is analyzed using a novel technique to detect a few informative thresholds for the construction of a low-dimensional attribute profile representing substantial spectral-spatial information. The proposed threshold detection method is computationally efficient. To assess the effectiveness of the proposed technique experiments are conducted on three real hyperspectral datasets using six different attributes and the results are compared to the recent state-of-the-art method. The results demonstrate that the proposed method has several advantages over the existing state-of-the-art method.
MouseGAN plus plus : Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Brain

Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding their relationships. Compared to a single MRI modality, multimodal MRI data provide complementary tissue features that can be exploited by deep learning models, resulting in better segmentation results. However, multimodal mouse brain MRI data is often lacking, making automatic segmentation of mouse brain fine structure a very challenging task. To address this issue, it is necessary to fuse multimodal MRI data to produce distinguished contrasts in different brain structures. Hence, we propose a novel disentangled and contrastive GAN-based framework, named MouseGAN++, to synthesize multiple MR modalities from single ones in a structure-preserving manner, thus improving the segmentation performance by imputing missing modalities and multi-modality fusion. Our results demonstrate that the translation performance of our method outperforms the state-of-the-art methods. Using the subsequently learned modality-invariant information as well as the modality-translated images, MouseGAN++ can segment fine brain structures with averaged dice coefficients of 90.0% (T2w) and 87.9% (T1w), respectively, achieving around +10% performance improvement compared to the state-of-the-art algorithms. Our results demonstrate that MouseGAN++, as a simultaneous image synthesis and segmentation method, can be used to fuse cross-modality information in an unpaired manner and yield more robust performance in the absence of multimodal data. We release our method as a mouse brain structural segmentation tool for free academic usage at https://github.com/yu02019.
Concomitant Ruxolitinib and Ibrutinib for Graft-Versus-Host Disease (GVHD): The First Reported Use in Pediatric Patients

Allogeneic hematopoietic stem cell transplant (alloHSCT) can be a life-saving treatment for patients with hematological disorders but far too often carries the feared complication of graft-versus-host disease (GVHD). The first-line treatment of GVHD is typically corticosteroids, but steroid-refractory chronic GVHD (cGVHD) has led to the Food and Drug Administration (FDA) approval of ruxolitinib (Jakafi), ibrutinib (Imbruvica), and belumosudil (Rezurock). Patient 1 was a four-year-old female diagnosed with natural killer (NK) cell dysfunction who underwent alloHSCT with cells from a 9/10 National Marrow Donor Program (NMDP) donor and subsequently developed chronic GVHD (cGVHD) of the skin and gut. This cGVHD was refractory to steroids and ibrutinib but improved with the administration of concomitant ibrutinib and ruxolitinib. Patient 2 was a one-year-old male with sickle cell anemia. The patient was transplanted under a haploidentical protocol from the mother but developed bronchiolitis obliterans organizing pneumonia (BOOP) and pathology-confirmed GVHD. This cGVHD was steroid-refractory and resolved with the administration of concomitant ibrutinib and ruxolitinib. To our knowledge, this is the first reported use of concomitant ruxolitinib and ibrutinib in pediatric patients. The combination was well tolerated with no significant adverse events. Neither patient had to discontinue these drugs. We propose a further investigation into this dual therapy in cGVHD either compared to steroids or as a second-line option.
MS-RMAC: Multiscale Regional Maximum Activation of Convolutions for Image Retrieval

Recent works have demonstrated that image descriptors produced by convolutional feature maps provide state-of-the-art performance for image retrieval and classification problems. However, features from a single convolutional layer are not robust enough for shape deformation, scale variation, and heavy occlusion. In this letter, we present a simple and straightforward approach for extracting multiscale (MS) regional maximum activation of convolutions features from different layers of the convolutional neural network. And we also propose aggregating MS features into a single vector by a parameter-free hedge method for image retrieval. Extensive experimental results on three challenging benchmark datasets indicate that the proposed method achieved outstanding performance against state-of-the-art methods.
Appropriate points choosing for subspace learning over image classification

Dimension reduction techniques are very important, as high-dimensional data are ubiquitous in many real-world applications, especially in this era of big data. In this paper, we propose a novel supervised dimensionality reduction method, called appropriate points choosing based DAG-DNE (Apps-DAG-DNE). In Apps-DAG-DNE, we choose appropriate points to construct adjacency graphs, for example, it chooses nearest neighbors to construct inter-class graph, which can build a margin between samples if they belong to the different classes, and chooses farthest points to construct intra-class graph, which can establish relationships between remote samples if and only if they belong to the same class. Thus, Apps-DAG-DNE could find a good representation for original data. To investigate the performance of Apps-DAG-DNE, we compare it with the state-of-the-art dimensionality reduction methods on Caltech-Leaves and Yale datasets. Extensive experimental demonstrates that the proposed Apps-DAG-DNE outperforms other dimensionality reduction methods and achieves state-of-the-art performance for image classification.
PD-L1 expression on immune cells, but not on tumor cells, is a favorable prognostic factor for patients with intrahepatic cholangiocarcinoma

Cholangiocarcinoma, the second most common liver malignancy, after hepatocarcinoma is highly aggressive and usually diagnosed in advanced cases. In the era of personalized medicine, targeted therapy protocols are limited for cholangiocarcinoma and the only potential curative treatment, surgical resection, is seldom applicable.This retrospective study included all cases with pathology-confirmed intrahepatic cholangiocarcinoma admitted in a tertiary healthcare facility during a 10-year timeframe. Clinical information, laboratory values, imaging studies, and survival data were retrieved, and PD-L1 immunostaining was performed on representative pathology slides, for each case. From the total of 136 included cases (49 surgical resections and 87 liver biopsies), 38.97% showed PD-L1 positivity on tumoral cells, 34.8% on tumor infiltrating immune cells, 10.11% on epithelial cells within the peritumoral area and 15.95% on immune cells from the peritumoral area. Overall survival was significantly higher in the first two scenarios. However, after adjusting for age, tumor number, tumor size, and tumor differentiation in a multivariate analysis, only PD-L1 positivity on tumor infiltrating immune cells remained a favorable prognostic for survival. High immune cell counts also correlated with increased overall survival.Our study demonstrated that PD-1/PD-L1 checkpoint pathway in the microenvironment of intrahepatic cholangiocarcinoma bears prognostic significance. PD-L1 expression on immune cells, in both resection and biopsy specimens, might be a strong independent predictor for a favorable outcome.
Task-Specific Image Partitioning

Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard to the task in hand. We propose a task-specific image partitioning framework to produce a region-based image representation that will lead to a higher task performance than that reached using any task-oblivious partitioning framework and existing supervised partitioning framework, albeit few in number. The proposed method partitions the image by means of correlation clustering, maximizing a linear discriminant function defined over a superpixel graph. The parameters of the discriminant function that define task-specific similarity/dissimilarity among superpixels are estimated based on structured support vector machine (S-SVM) using task-specific training data. The S-SVM learning leads to a better generalization ability while the construction of the superpixel graph used to define the discriminant function allows a rich set of features to be incorporated to improve discriminability and robustness. We evaluate the learned task-aware partitioning algorithms on three benchmark datasets. Results show that task-aware partitioning leads to better labeling performance than the partitioning computed by the state-of-the-art general-purpose and supervised partitioning algorithms. We believe that the task-specific image partitioning paradigm is widely applicable to improving performance in high-level image understanding tasks.
Ultra-wideband elliptical slot antenna fed by tapered microstrip line with U-shaped tuning stub

A printed elliptical slot antenna fed by tapered microstrip line with U-shaped tuning stub is proposed for ultra-wideband (UWB) applications in this paper. The design parameters for achieving optimal performance art investigated. The operation and characteristics of the proposed antenna are also analyzed. Good agreement is obtained between the simulation and the experiment. (c) 2005 Wiley Periodicals, Inc.
Large-scale image retrieval using transductive support vector machines

In this paper, we propose a new method for large-scale image retrieval by using binary hierarchical trees and transductive support vector machines (TSVMs). We create multiple hierarchical trees based on the separability of the visual object classes, and TSVM classifier is used to find the hyperplane that best separates both the labeled and unlabeled data samples at each node of the binary hierarchical trees (BHTs). Then the separating hyperplanes returned by TSVM are used to create binary codes or to reduce the dimension. We propose a novel TSVM method that is more robust to the noisy labels by interchanging the classical Hinge loss with the robust Ramp loss. Stochastic gradient based solver is used to learn TSVM classifier to ensure that the method scales well with large-scale data sets. The proposed method significantly improves the Euclidean distance metric and achieves comparable results to the state-of-the-art on CIFAR10 and MNIST data sets, and significantly outperforms the state-of-the-art hashing methods on more challenging ImageCLEF 2013, NUS-WIDE, and CIFAR100 data sets. (C) 2017 Elsevier Inc. All rights reserved.
Sn-Based Perovskite Halides for Electronic Devices

Because of its less toxicity and electronic structure analogous to that of lead, tin halide perovskite (THP) is currently one of the most favorable candidates as an active layer for optoelectronic and electric devices such as solar cells, photodiodes, and field-effect transistors (FETs). Promising photovoltaics and FETs performances have been recently demonstrated because of their desirable electrical and optical properties. Nevertheless, THP's easy oxidation from Sn2+ to Sn4+ , easy formation of tin vacancy, uncontrollable film morphology and crystallinity, and interface instability severely impede its widespread application. This review paper aims to provide a basic understanding of THP as a semiconductor by highlighting the physical structure, energy band structure, electrical properties, and doping mechanisms. Additionally, the key chemical instability issues of THPs are discussed, which are identified as the potential bottleneck for further device development. Based on the understanding of the THPs properties, the key recent progress of THP-based solar cells and FETs is briefly discussed. To conclude, current challenges and perspective opportunities are highlighted.
Fuzzy rule based unsupervised sentiment analysis from social media posts

In this paper, we compute the sentiment of social media posts using a novel set of fuzzy rules involving multiple lexicons and datasets. The proposed fuzzy system integrates Natural Language Processing techniques and Word Sense Disambiguation using a novel unsupervised nine fuzzy rule based system to classify the post into: positive, negative or neutral sentiment class. We perform a comparative analysis of our method on nine public twitter datasets, three sentiment lexicons, four state-of-the-art approaches for unsupervised Sentiment Analysis and one state-of-the-art method for supervised machine learning. Traditionally, Sentiment Analysis of twitter data is performed using a single lexicon. Our results can give an insight to researchers to choose which lexicon is best for social media. The fusion of fuzzy logic with lexicons for sentiment classification provides a new paradigm in Sentiment Analysis. Our method can be adapted to any lexicon and any dataset (two-class or three-class sentiment). The experiments on benchmark datasets yield higher performance for our approach as compared to the state-of-the-art. (C) 2019 Elsevier Ltd. All rights reserved.
Personal Fabrication: Patrick Baudisch and Stefanie Mueller Talk About Physical Natives

Personal fabrication is celebrating technological break-throughs that will enable us to easily generate new physical forms and shapes. It democratizes the capability of building physical things and may usher in the era of physical natives. Stefanie Mueller and Patrick Baudisch critically review the state of the art and speculate on the future use and users of personal fabrication, while highlighting corresponding research challenges for the HCI community.
Mono- and Dual-Regulated Contractive-Expansive-Contractive Deep Convolutional Networks for Classification of Multispectral Remote Sensing Images

Deep convolutional neural networks (CNNs) are the state-of-the-art methods in the domain of classification of remote sensing (RS) data. However, traditional CNN models suffer from huge computational costs in learning land-use and land-cover features, particularly in large-scale RS problems. To address this issue, we propose a reliable mono- and dual-regulated contractive-expansive-contractive (MRCEC/DRCEC) CNN for scene based multispectral (MS) image classification. The proposed technique increases the accuracy of learning and minimizes the loss in the feature maps by incorporating the CEC approach in the classification. Extensive experiments conducted on the Sentinel-2 EuroSATallbands dataset pointed out that the proposed model outperforms the state-of-the-art models such as EfficientNet-B0, RESNet-50, and EfficientNet-B7.
Hardening Principles and Characterization of an Optocoupler Including a Vertical Cavity Surface Emitting Laser

This paper reports the hardening principles and the first characterization of an optocoupler structure that uses a Vertical Cavity Surface Emitting Laser (VCSEL) as a light emitter. Current Transfer Ratio (CTR) measurements carried out before and after proton irradiations show that this architecture improves the radiation hardness by a factor of 10 or more above the present state-of-the-art hardened optocouplers.
Insights into the effect mechanism of back-mixing inoculation on sewage sludge biodrying process: Biodrying characteristics and microbial community succession

Back mixing was frequently used to replace conventional bulking agenting, however, however, the internal effect mechanism was unclear. This study compared four bulking agents: mushroom residue (MR), MR + primary BM (BM-P), BM-P, and secondary BM (BM-S). The effect mechanism of back mixing (BM) inoculation was assessed based on biodrying performance and microbial community succession. Four trials (Trial A, Trial B, Trial C, and Trial D) reached maximum temperatures of 61.9, 68.8, 73.7, and 69.9 °C on days 6, 3, 2, and 2, respectively. Application of BM increased pile warming rate and resulted in higher temperatures. Temperature changes and microbial competition lead to decline in microbial diversity and richness during the biodrying process. Microbial diversity increased of four biodried products. The number of microorganisms shared by Trial A, Trial B, Trial C, and Trial D were 90, 119, 224, and 300, respectively. The addition of BM improved microbial community stability, and facilitating the initiation of biodrying process. Microbial genera that played an important role in the biodrying process included Ureibacillus, Bacillus, Sphaerobacter, and Tepidimicrobium. Based on these results, it was concluded that BM was efficient method to enhanced the microbial activity and reduced the usage of bulking agent.
Systematic review on ensuring the global food security and covid-19 pandemic resilient food systems: towards accomplishing sustainable development goals targets

Covid-19, one of the most critical and widespread global pandemics, has resulted in extraordinary risk corollaries engulfing millions of people's lives and has caused an unprecedented economic downturn while amplifying food insecurity. A systematic review of 132 scientific communications was performed over a 15-year period, using articles from the ScienceDirect and Web of Science databases (2006-2021). In addition, 24 policy briefs, country papers, and publications from the UN, WHO, FAO, and OECD were cited. The aim of this paper is to provide a comprehensive review of existing literature on the adverse effects of the Covid-19 pandemic on agricultural food systems, as well as potential strategies for building robust, resilient, and sustainable food systems to ensure global food security, safety, and endeavors regarding future global emergencies, as well as new research policies while achieving SDG targets. This would fill a research gap while also having long-term implications for health, agricultural, and food resilience policy development in a rapidly changing world. Covid-19 demonstrates how human, animal, and environmental health are all interconnected, emphasizing the need for one health legislation and a paradigm shift in planetary health. Furthermore, it identifies potential mechanisms for rebuilding better systems by shifting priorities toward policy coherence, innovative food system governance, re-engineering market access, and nexus thinking in the food system approach. According to our findings, the COVID-19 posed unavoidable impediments to achieving SDG targets for food security and household poverty.
Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis

Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung nodule is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score regression. However, this is quite challenging due to the considerable difficulty of lung nodule heterogeneity modeling and the limited discrimination capability on ambiguous cases. To solve these challenges, we propose a Multi-Task deep model with Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. Compared to existing methods which consider these two tasks separately, the relatedness between lung nodule classification and attribute score regression is explicitly explored in a cause-and-effect manner within our multi-task deep model, which can contribute to the performance gains of both tasks. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. Furthermore, a Siamese network with a margin ranking loss is elaborately designed to enhance the discrimination capability on ambiguous nodule cases. To further explore the internal relationship between two tasks and validate the effectiveness of the proposed model, we use the recursive feature elimination method to iteratively rank the most malignancy-related features. We validate the efficacy of our method MTMR-Net on the public benchmark LIDC-IDRI dataset. Extensive experiments show that the diagnosis results with internal relationship explicitly explored in our model has met some similar patterns in clinical usage and also demonstrate that our approach can achieve competitive classification performance and more accurate scoring on attributes over the state-of-the-arts. Codes are publicly available at: https://github.com/CaptainWilliam/MTMR-NET.
Pyrazolopyrimidines: Potent Inhibitors Targeting the Capsid of Rhino- and Enteroviruses

There are currently no drugs available for the treatment of enterovirus (EV)-induced acute and chronic diseases such as the common cold, meningitis, encephalitis, pneumonia, and myocarditis with or without consecutive dilated cardiomyopathy. Here, we report the discovery and characterization of pyrazolopyrimidines, a well-tolerated and potent class of novel EV inhibitors. The compounds inhibit the replication of a broad spectrum of EV in vitro with IC50 values between 0.04 and 0.64 μM for viruses resistant to pleconaril, a known capsid-binding inhibitor, without affecting cytochrome P450 enzyme activity. Using virological and genetics methods, the viral capsid was identified as the target of the most promising, orally bioavailable compound 3-(4-trifluoromethylphenyl)amino-6-phenylpyrazolo[3,4-d]pyrimidine-4-amine (OBR-5-340). Its prophylactic as well as therapeutic application was proved for coxsackievirus B3-induced chronic myocarditis in mice. The favorable pharmacokinetic, toxicological, and pharmacodynamics profile in mice renders OBR-5-340 a highly promising drug candidate, and the regulatory nonclinical program is ongoing.
High performance scalable image coding

A high performance scalable image coding algorithm is proposed. The salient features of this algorithm are the ways to form and locate the significant clusters. Thanks to the list structure, the new coding algorithm achieves fine fractional bit-plane coding with negligible additional complexity. Experiments show that it performs comparably or better than the state-of-the-art coders. Furthermore, the flexible codec supports both quality and resolution scalability, which is very attractive in many network applications.