ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Constrained feature selection for semisupervised color-texture image segmentation using spectral clustering

Color-texture image segmentation remains a challenging problem due to extensive color-texture variability. Thus, the limited prior knowledge that is expressed by pairwise constraints can be exploited to guide the segmentation process. We propose a new semisupervised method by combining constrained feature selection and spectral clustering (SC) to perform color-texture image segmentation. The pairwise constraints are used by the constraint feature selection to choose the most relevant features among an available set of color and texture features. For this purpose, an innovative constraint score is developed to evaluate a subset of features at one time. A specific constrained SC algorithm involving the pairwise constraints is then applied to regroup the pixels into clusters. Experimental results on four benchmark datasets show that the proposed constraint score outperforms the main state-of-the-art constraint scores and that our semisupervised segmentation method is competitive compared with supervised, semisupervised, and unsupervised state-of-the-art segmentation methods. ? 2021 SPIE and IS&T [DOI: 10 .1117/1.JEI.30.1.013014]
Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks

As one of the most commonly used features, Mel-frequency cepstral coefficients (MFCCs) are less discriminative at high frequency. A novel technique, known as Deep scattering spectrum (DSS), addresses this issue and looks to preserve greater details. DSS feature has shown promise both on classification and recognition tasks. In this paper, we extend the use of DSS feature for acoustic scene classification task. Results on Detection and classification of acoustic scenes and events (DCASE) 2016 and 2017 show that DSS provided 4:8% and 17:4% relative improvements in accuracy over MFCC features, within a state-of-the-art time delay neural network framework.
Evaluating the effect of LPS from periodontal pathogenic bacteria on the expression of senescence-related genes in human dental pulp stem cells

The human dental pulp stem cells (hDPSCs) are one of the readily available sources of multipotent mesenchymal stem cells (MSCs) and can be considered as a type of tool cells for cell-based therapies. However, the main limitation in the clinical use of these cells is DPSC senescence, which can be induced by lipopolysaccharide (LPS) of oral pathogenic bacteria. Up to now, far little attention has been paid to exploring the molecular mechanisms of senescence in DPSCs. So, the current study aimed to investigate the underlying molecular mechanism of senescence in hDPSCs stimulated with Porphyromonas gingivalis (P. gingivalis) and Escherichia coli (E. coli)-derived LPSs, by evaluating both mRNA and protein expression of four important senescence-related genes, including TP53, CDKN1A, CDKN2A and SIRT1. To this purpose, hDPSCs were stimulated with different LPSs for 6, 24 and 48 h and then the gene expression was evaluated using quantitative real-time polymerase chain reaction (qPCR) and western blotting. Following stimulation with P. gingivalis and E. coli-derived LPSs, the relative mRNA and protein expression of all genes were significantly up-regulated in a time-dependent manner, as compared with unstimulated hDPSCs. Moreover, the hDPSCs stimulated with P. gingivalis LPS for 6 and 24 h had the highest mRNA expression of CDKN1A and SIRT1, respectively (p &lt; 0.0001), whereas the highest mRNA expression of CDKN2A and TP53 was seen in hDPSCs stimulated with E. coli LPS for 48 h (p &lt; 0.0001). In summary, because DPSCs have been reported to have therapeutic potential for several cell-based therapies, targeting molecular mechanisms aiming at preventing DPSC senescence could be considered a valuable strategy.
'Depending on where I am…' Hair, travelling and the performance of identity among Black and mixed-race women

A growing interdisciplinary literature examines the role of hair textures and styles in Black and mixed-race women's identity performances. Through an analysis of travel narratives, this paper extends and complements research on the context-dependency of racialized identity performances. This paper presents an analysis of 24 qualitative interviews with Black and mixed-race women in England and Germany. The question it seeks to answer is: 'How do changes in context alter Black and mixed-race women's hairstyling practices as a performance of identity?' Navigating a novel context could lead the women to (1) conform to local standards of beauty and femininity, (2) resist external expectations, (3) try out novel performances and (4) negotiate the complex performance of belonging. All in all, this paper shows that Black and mixed-race women dialogically re/negotiated and performatively re/created how they identify and how they are identified by others as they moved from one context to another.
Regulatory aspects on nanomedicines

Nanomedicines have been in the forefront of pharmaceutical research in the last decades, creating new challenges for research community, industry, and regulators. There is a strong demand for the fast development of scientific and technological tools to address unmet medical needs, thus improving human health care and life quality. Tremendous advances in the biomaterials and nanotechnology fields have prompted their use as promising tools to overcome important drawbacks, mostly associated to the non-specific effects of conventional therapeutic approaches. However, the wide range of application of nanomedicines demands a profound knowledge and characterization of these complex products. Their properties need to be extensively understood to avoid unpredicted effects on patients, such as potential immune reactivity. Research policy and alliances have been bringing together scientists, regulators, industry, and, more frequently in recent years, patient representatives and patient advocacy institutions. In order to successfully enhance the development of new technologies, improved strategies for research-based corporate organizations, more integrated research tools dealing with appropriate translational requirements aiming at clinical development, and proactive regulatory policies are essential in the near future. This review focuses on the most important aspects currently recognized as key factors for the regulation of nanomedicines, discussing the efforts under development by industry and regulatory agencies to promote their translation into the market. Regulatory Science aspects driving a faster and safer development of nanomedicines will be a central issue for the next years.
The state of the art of environmental valuation with discrete choice experiments

This paper provides with a review of the state of the art of environmental valuation with discrete choice experiments (DCEs). The growing body of literature on this field serves to emphasize the increasing role that DCEs are playing in environmental decision making in the last decade. The paper attempts to cover the full process of undertaking a choice experiment, including survey and experimental design, econometric analysis of choice data and welfare analysis. The research on this field is found to be intense, although many challenges are put forward (e.g. choice-task complexity and cognitive effort, experimental design, preference and scale heterogeneity, endogeneity or model uncertainty). Reviewing the state of the art of DCEs serves to draw attention to the main challenges that this methodological approach will need to overcome in the coming years and to identify the frontiers in discrete choice analysis. (C) 2010 Elsevier B.V. All rights reserved.
A Computational Approach to Packet Classification

Multi-field packet classification is a crucial component in modern software-defined data center networks. To achieve high throughput and low latency, state-of-the-art algorithms strive to fit the rule lookup data structures into on-die caches; however, they do not scale well with the number of rules. We present a novel approach, NuevoMatch, which improves the memory scaling of existing methods. A new data structure, Range Query Recursive Model Index (RQ-RMI), is the key component that enables NuevoMatch to replace most of the accesses to main memory with model inference computations. We describe an efficient training algorithm that guarantees the correctness of the RQ-RMI-based classification. The use of RQ-RMI allows the rules to be compressed into neural networks that fit into the hardware cache. Further, it takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector instructions, achieving a latency of tens of nanoseconds per lookup. Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geometric mean compression factor of 4.9x, 8x, and 82x, and average performance improvement of 2.4x, 2.6x, and 1.6x in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.
State of the art in nail dosimetry: free radicals identification and reaction mechanisms

Until very recently, analysis of bone biopsies by means of the method of electron paramagnetic resonance (EPR) collected after surgery or amputation has been considered as the sole reliable method for radiation dose assessment in hands and feet. EPR measurements in finger- and toenail have been considered for accident dosimetry for a long time. Human nails are very attractive biophysical materials because they are easy to collect and pertinent to whole body irradiation. Information on the existence of a radiation-induced signal in human nails has been reported almost 25 years ago. However, no practical application of EPR dosimetry on nails is known to date because, from an EPR perspective, nails represent a very complex material. In addition to the radiation-induced signal (RIS), parasitic and intense signals are induced by the mechanical stress caused when collecting nail samples (mechanically induced signals-MIS). Moreover, it has been demonstrated that the RIS stability is strongly influenced not only by temperature but also by humidity. Most studies of human nails were carried out using conventional X-band microwave band (9 GHz). Higher frequency Q-band (37 GHz) provides higher spectral resolution which allows obtaining more detailed information on the nature of different radicals in human nails. Here, we present for the first time a complete description of the different EPR signals identified in nails including parasitic, intrinsic and RIS. EPR in both X- and Q-bands was used. Four different MIS signals and five different signals specific to irradiation with ionizing radiation have been identified. The most important outcome of this work is the identification of a stable RIS component. In contrast with other identified (unstable) RIS components, this component is thermally and time stable and not affected by the physical contact of fingernails with water. A detailed description of this signal is provided here. The discovery of stable radiation-induced radical(s) associated with the RIS component mentioned opens a way for broad application of EPR dosimetry in human nails. Consequently, several recent dosimetry assessments of real accident cases have been performed based on the described measurements and analyses of this component.
What have we learnt from palaeoclimate simulations?

There has been a gradual evolution in the way that palaeoclimate modelling and palaeoenvironmental data are used together to understand how the Earth System works, from an initial and largely descriptive phase through explicit hypothesis testing to diagnosis of underlying mechanisms. Analyses of past climate states are now regarded as integral to the evaluation of climate models, and have become part of the toolkit used to assess the likely realism of future projections. Palaeoclimate assessment has demonstrated that changes in large-scale features of climate that are governed by the energy and water balance show consistent responses to changes in forcing in different climate states, and these consistent responses are reproduced by climate models. However, state-of-the-art models are still largely unable to reproduce observed changes in climate at a regional scale reliably. While palaeoclimate analyses of state-of-the-art climate models suggest an urgent need for model improvement, much work is also needed on extending and improving palaeoclimate reconstructions and quantifying and reducing both numerical and interpretative uncertainties. Copyright (C) 2016 The Authors. Journal of Quaternary Science Published by John Wiley & Sons Ltd.
Programming Multistable Metamaterials to Discover Latent Functionalities

Using multistable mechanical metamaterials to develop deployable structures, electrical devices, and mechanical memories raises two unanswered questions. First, can mechanical instability be programmed to design sensors and memory devices? Second, how can mechanical properties be tuned at the post-fabrication stage via external stimuli? Answering these questions requires a thorough understanding of the snapping sequences and variations of the elastic energy in multistable metamaterials. The mechanics of deformation sequences and continuous force/energy-displacement curves are comprehensively unveiled here. A 1D array, that is chain, of bistable cells is studied to explore instability-induced energy release and snapping sequences under one external mechanical stimulus. This method offers an insight into the programmability of multistable chains, which is exploited to fabricate a mechanical sensor/memory with sampling (analog to digital-A/D) and data reconstruction (digital to analog-D/A) functionalities operating based on the correlation between the deformation sequence and the mechanical input. The findings offer a new paradigm for developing programmable high-capacity read-write mechanical memories regardless of thei size scale. Furthermore, exotic mechanical properties can be tuned by harnessing the attained programmability of multistable chains. In this respect, a transversely multistable mechanical metamaterial with tensegrity-like bistable cells is designed to showcase the tunability of chirality.
The Disparities in Mental Health Between Gay and Bisexual Men Following Positive HIV Diagnosis in China: A One-Year Follow-Up Study

This study aimed to determine the change in mental health (depression and anxiety) among HIV-positive gay and bisexual men (GBM) one year after diagnosis and the disparities in trajectories of mental health between them. The potential factors contributing to the disparities were also investigated. This was a one-year follow-up study focusing on the mental health of newly diagnosed HIV-positive individuals. Participants rated their depression, anxiety, stress, and social support levels at baseline and one year later. Information on the utilization of mental healthcare and the initiation of antiretroviral therapy (ART) after diagnosis was collected at one-year follow-up. A total of 171 and 87 HIV-positive gay and bisexual men, respectively, completed two-time points surveys in this study. The depressive and anxiety symptoms experienced by HIV-positive GBM improvement one year after diagnosis. These improvements tended to be smaller in gay participants. Other factors including mental health care utilization and ART status during the one-year follow-up period, changes in social stress scores and objective social support scores were also associated with the changes in depression and anxiety, and all these factors, except for change in objective support, were found to be statistically different between HIV-positive GBM. Special attention should be given to the mental health of HIV-positive gay men. Promoting HIV-positive gay men to assess to mental health services and ART may be important for these populations to improve mental health. Enhancing social support and reducing stress levels may also be necessary for the vulnerable HIV-positive sexual minority groups.
Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules

In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early identification of failures has become crucial to maintain productivity and prolong components' life. Of all defects, cell-level anomalies can lead to serious failures and may affect surrounding PV modules in the long run. These fine defects are usually captured with high spatial resolution electroluminescence (EL) imaging. The difficulty of acquiring such images has limited the availability of data. For this work, multiple data resources and augmentation techniques have been used to surpass this limitation. Current state-of-the-art detection methods extract barely low-level information from individual PV cell images, and their performance is conditioned by the available training data. In this article, we propose an end-to-end deep learning pipeline that detects, locates and segments cell-level anomalies from entire photovoltaic modules via EL images. The proposed modular pipeline combines three deep learning techniques: 1. object detection (modified Faster-RNN), 2. image classification (EfficientNet) and 3. weakly supervised segmentation (autoencoder). The modular nature of the pipeline allows to upgrade the deep learning models to the further improvements in the state-of-the-art and also extend the pipeline towards new functionalities.
An ART-based fuzzy controller for the adaptive navigation of a quadruped robot

An adaptive-resonance theory (ART)-based fuzzy controller is presented for the adaptive navigation of a quadruped robot in cluttered environments, by incorporating the capability of ART in stable category recognition into fuzzy-logic control for selecting the adequate rule base. The environment category and the navigation mechanism are first described for the quadruped robot. The ART-based fuzzy controller, including an ART-based environment recognizer, a comparer, combined rule bases, and a fuzzy inferring mechanism, is next introduced for the purpose of the adaptive navigation of the quadruped robot. Unlike classical/conventional adaptive-fuzzy controllers, the present adaptive-control scheme is implemented by the adaptive selection of fuzzy-rule base in response to changes of the robot environment, which can be categorized and recognized by the proposed environment recognizer. The results of simulation and experiment show that the adaptive-fuzzy controller is effective.
A Multimode CMOS Vision Sensor With On-Chip Motion Direction Detection and Simultaneous Energy Harvesting Capabilities

This work presents a 128 x 128 multi-function CMOS vision sensor with motion direction detection (MDD) and simultaneous energy harvesting (EH) capability, featuring: 1) light weight template-based optical flow (OF) algorithm for on-chip MDD with high energy efficiency and small area overhead; and 2) independent imaging and energy harvesting (EH) operations using vertically stacked N+/PW and PW/DNW+DNW/PSUB junctions without photodiode reconfiguration. Fabricated in 0.18 mu m standard CMOS, this work demonstrates the first CMOS vision sensor featuring both MDD and EH modes within a compact pixel pitch of 4.75 mu m. With a power consumption of 10.5 mu W at 15fps in MDD mode, the chip prototype achieves a motion sensing figure-of-merit (MS-FoM) of 171 pJ/pixel.frame, which is 30% better than the state-of-the-art MDD sensors. The proposed EH configuration achieves continuous power generation at 267pW/Ix/mm(2) irrespective of the imaging/MDD operation, demonstrating a 1.56x improvement over prior arts.
Exploring Segment-Level Semantics for Online Phase Recognition From Surgical Videos

Automatic surgical phase recognition plays a vital role in robot-assisted surgeries. Existing methods ignored a pivotal problem that surgical phases should be classified by learning segment-level semantics instead of solely relying on frame-wise information. This paper presents a segment-attentive hierarchical consistency network (SAHC) for surgical phase recognition from videos. The key idea is to extract hierarchical high-level semantic-consistent segments and use them to refine the erroneous predictions caused by ambiguous frames. To achieve it, we design a temporal hierarchical network to generate hierarchical high-level segments. Then, we introduce a hierarchical segment-frame attention module to capture relations between the low-level frames and high-level segments. By regularizing the predictions of frames and their corresponding segments via a consistency loss, the network can generate semantic-consistent segments and then rectify the misclassified predictions caused by ambiguous low-level frames. We validate SAHC on two public surgical video datasets, i.e., the M2CAI16 challenge dataset and the Cholec80 dataset. Experimental results show that our method outperforms previous state-of-the-arts and ablation studies prove the effectiveness of our proposed modules. Our code has been released at: https://github.com/xmed-lab/SAHC.
Edge-preserving colour-to-greyscale conversion

This study introduces a colour-to-greyscale conversion method using colour clustering and multidimensional scaling that preserves region integrity, that is, it preserves edge structure without low-frequency distortion. Edges can be extracted from the resulting greyscale images and the results will be close to those obtainable from the original colour image. We compare with prior-art colour-to-greyscale conversion and a recent colour-edge extractor and show superior performance in each case.
Combining metadata and co-citations for recommending related papers

Identification of relevant documents is performed to keep track of the state-of-the-art methods and relies on research paper recommender systems. The proposed approaches for these systems can be classified into categories like content-based, collaborative filtering-based, and bibliographic information-based approaches. The content-based approaches exploit the full text of articles and provide more promising results than other approaches. However, most content is not freely available because of subscription requirements. Therefore, the scope of content-based approaches is limited. In such scenarios, the best possible alternative could be the exploitation of other openly available resources. Therefore, this research explores the possible use of metadata and bibliographic information to find related articles. The approach incorporates metadata with co-citations to find and rank related articles against a query paper. The similarity score of metadata fields is calculated and combined with co-citations. The proposed approach is evaluated on a newly constructed dataset of 5116 articles. The benchmark ranking against each co-cited document set is established by applying Jensen-Shannon divergence (JSD) and results are evaluated with the state-of-the-art content-based approach in terms of normalized discounted cumulative gain (NDCG). The state-of-the-art content-based approach achieved an NDCG score of 0.86 while the traditional co-citation-based approach scored 0.72. The presented method achieved NDCG scores of 0.73, 0.77, and 0.78 by incorporating the title, co-citation and title, and abstract, respectively, whereas the highest NDCG score of 0.77 was achieved by combining co-citations with metadata. However, better results are achieved by incorporating the title and abstract with NDCG score of 0.81. Therefore, it can be concluded that the proposed approach could be a better alternative in cases where content is unavailable.