ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Molecular hallmarks of long non-coding RNAs in aging and its significant effect on aging-associated diseases

Aging is linked to the deterioration of many physical and cognitive abilities and is the leading risk factor for Alzheimer's disease. The growing aging population is a significant healthcare problem globally that researchers must investigate to better understand the underlying aging processes. Advances in microarrays and sequencing techniques have resulted in deeper analyses of diverse essential genomes (e.g., mouse, human, and rat) and their corresponding cell types, their organ-specific transcriptomes, and the tissue involved in aging. Traditional gene controllers such as DNA- and RNA-binding proteins significantly influence such programs, causing the need to sort out long non-coding RNAs, a new class of powerful gene regulatory elements. However, their functional significance in the aging process and senescence has yet to be investigated and identified. Several recent researchers have associated the initiation and development of senescence and aging in mammals with several well-reported and novel long non-coding RNAs. In this review article, we identified and analyzed the evolving functions of long non-coding RNAs in cellular processes, including cellular senescence, aging, and age-related pathogenesis, which are the major hallmarks of long non-coding RNAs in aging.
Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach

Establishing accurate electrical load prediction is vital for pricing and power system management. However, the unpredictable behavior of private and industrial users results in uncertainty in these power systems. Furthermore, the utilization of renewable energy sources, which are often variable in their production rates, also increases the complexity making predictions even more difficult. In this paper an interval type-2 intuitionist fuzzy logic system whose parameters are trained in a hybrid fashion using gravitational search algorithms with the ridge least square algorithm is presented for short-term prediction of electrical loading. Simulation results are provided to compare the performance of the proposed approach with that of state-of-the-art electrical load prediction algorithms for Poland, and five regions of Australia. The simulation results demonstrate the superior performance of the proposed approach over seven different current state-of-the-art prediction algorithms in the literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO.
Concentrations, sources, and health risk assessment of metals in indoor dust collected from visual arts studios of selected tertiary institutions in Southern Nigeria

This study provides data on the concentrations and occupational risk of Cd, Pb, Cr, Ni, Cu, Co, Mn, Zn, and Fe in indoor dust from visual arts studios of nine tertiary institutions in southern Nigeria. The dust samples were digested in aqua regia and their metal concentrations were quantified by atomic absorption spectrometry. The concentrations of metals in dust from visual arts studios ranged from not detected (nd) to 91.5 mg kg(-1) Cd, 4.5 to 540 mg kg(-1) Pb, 0.10 to 1,100 mg kg(-1) Cr, 0.50 to 1,150 mg kg(-1) Ni, 10 to 15,600 mg kg(-1) Cu, 0.5 to 146 mg kg(-1) Co, 3.0 to 3,680 mg kg(-1) Mn, 93.5 to 9,600 mg kg(-1) Zn, and 853 to 237,000 mg kg(-1) Fe. The degree of contamination of the dust particles by metals was assessed by making use of the contamination/pollution index, geoaccumulation index and enrichment factor. These indices suggested that dusts from these visual arts studios were impacted with Cd, Pb, Cu, and Zn. The hazard index (HI) and cancer risk values relating to adults' exposure to metals in dust from these arts studios were within safe limits. Principal component analysis indicated that the sources of metal contamination in dust from these visual arts studios were related to inputs from constituents of the artist's materials and vehicular emissions.
A Review on Organic Fluorimetric and Colorimetric Chemosensors for the Detection of Ag(I) Ions

Organic compounds display several electronic and structural features which enable their application in various fields, ranging from biological to non-biological. These compounds contain heteroatoms like sulfur, nitrogen and oxygen, which provide coordination sites to act as ligands in the field of coordination chemistry and are used as chemosensors to detect various metal ions. This review article covers different organic compounds including thiourea, Schiff base, pyridine, thiophene, coumarin, triazolyl pyrenes, imidazole, fluorescein, thiazole, tricarbocyanine, rhodanine, porphyrin, hydrazone, benzidine and other functional groups based chemosensors, that contain heteroatoms like sulfur, nitrogen and, oxygen for fluorimetric and colorimetric detection of Ag+ in different environmental, agricultural, and biological samples. Further, the sensing mechanism and performances of these chemosensors have been discussed, which could help the readers for the future design of highly efficient, selective, and sensitive chemosensors for the detection and determination of Ag+ ions.
Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting

For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing them by classification algorithms. To this end, we propose an integrated framework consisting of a novel supervised cell-image segmentation algorithm and a new touching-cell splitting method. For the segmentation part, we segment the cell regions from the other areas by classifying the image pixels into either cell or extra-cellular category. Instead of using pixel color intensities, the color-texture extracted at the local neighborhood of each pixel is utilized as the input to our classification algorithm. The color-texture at each pixel is extracted by local Fourier transform (LFT) from a new color space, the most discriminant color space (MDC). The MDC color space is optimized to be a linear combination of the original RGB color space so that the extracted LFT texture features in the MDC color space can achieve most discrimination in terms of classification (segmentation) performance. To speed up the texture feature extraction process, we develop an efficient LFT extraction algorithm based on image shifting and image integral. For the splitting part, given a connected component of the segmentation map, we initially differentiate whether it is a touching-cell clump or a single nontouching cell. The differentiation is mainly based on the distance between the most likely radial-symmetry center and the geometrical center of the connected component. The boundaries of touching-cell clumps are smoothed out by Fourier shape descriptor before carrying out an iterative, concave-point and radial-symmetry based splitting algorithm. To test the validity, effectiveness and efficiency of the framework, it is applied to follicular lymphoma pathological images, which exhibit complex background and extracellular texture with nonuniform illumination condition. For comparison purposes, the results of the proposed segmentation algorithm are evaluated against the outputs of superpixel, graph-cut, mean-shift, and two state-of-the-art pathological image segmentation methods using ground-truth that was established by manual segmentation of cells in the original images. Our segmentation algorithm achieves better results than the other compared methods. The results of splitting are evaluated in terms of under-splitting, over-splitting, and encroachment errors. By summing up the three types of errors, we achieve a total error rate of 5.25% per image.
Deep Renyi entropy graph kernel

Graph kernels are applied heavily for the classification of structured data. In this paper, we propose a deep Renyi entropy graph kernel for this purpose. We gauge the deep information through a family of h-layer expansion subgraphs rooted at a vertex, and define a h-layer depth-based second-order Renyi entropy representation for each vertex. The second-order Renyi entropy representation is used together with Euclidean distance to build a deep second-order Renyi entropy graph kernel (SREGK). For graphs with n vertices, the time complexity for our kernel is O(n(3)). This low-order polynomial complexity enables our subgraph kernels to easily scale up to graphs of reasonably large sizes and thus overcome the size limits arising in state-of-the-art graph kernels. Experimental results on fourteen real world graph datasets are shown to demonstrate the overall superior performance of our approach over a number of state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.
Real-time tracking based on deep feature fusion

Deep learning-based methods have recently attracted significant attention in visual tracking community, leading to an increase in state-of-the-art tracking performance. However, due to the utilization of more complex models, it has also been accompanied with a decrease in speed. For real-time tracking applications, a careful balance of performance and speed is required. We propose a real-time tracking method based on deep feature fusion, which combines deep learning with kernel correlation filter. First, hierarchical features are extracted from a lightweight pre-trained convolutional neural network. Then, original features of different levels are fused using canonical correlation analysis. Fused features, as well as some original deep features, are used in three kernel correlation filters to track the target. An adaptive update strategy, based on dispersion analysis of response maps for the correlation filters, is proposed to improve robustness to target appearance changes. Different update frequencies are adopted for the three filters to adapt to severe appearance changes. We perform extensive experiments on two benchmarks: OTB-50 and OTB-100. Quantitative and qualitative evaluations show that the proposed tracking method performs favorably against some state-of-the-art methods - even better than algorithms using complex network model. Furthermore, proposed algorithm runs faster than 20 frame per second (FPS) and hence able to achieve near real-time tracking.
Improving Large-Scale Image Retrieval Through Robust Aggregation of Local Descriptors

Visual search and image retrieval underpin numerous applications, however the task is still challenging predominantly due to the variability of object appearance and ever increasing size of the databases, often exceeding billions of images. Prior art methods rely on aggregation of local scale-invariant descriptors, such as SIFT, via mechanisms including Bag of Visual Words (BoW), Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vectors (FV). However, their performance is still short of what is required. This paper presents a novel method for deriving a compact and distinctive representation of image content called Robust Visual Descriptor with Whitening (RVD-W). It significantly advances the state of the art and delivers world-class performance. In our approach local descriptors are rank-assigned to multiple clusters. Residual vectors are then computed in each cluster, normalized using a direction-preserving normalization function and aggregated based on the neighborhood rank. Importantly, the residual vectors are de-correlated and whitened in each cluster before aggregation, leading to a balanced energy distribution in each dimension and significantly improved performance. We also propose a new post-PCA normalization approach which improves separability between the matching and non-matching global descriptors. This new normalization benefits not only our RVD-W descriptor but also improves existing approaches based on FV and VLAD aggregation. Furthermore, we show that the aggregation framework developed using hand-crafted SIFT features also performs exceptionally well with Convolutional Neural Network (CNN) based features. The RVD-W pipeline outperforms state-of-the-art global descriptors on both the Holidays and Oxford datasets. On the large scale datasets, Holidays1M and Oxford1M, SIFT-based RVD-W representation obtains a mAP of 45.1 and 35.1 percent, while CNN-based RVD-W achieve a mAP of 63.5 and 44.8 percent, all yielding superior performance to the state-of-the-art.
Charge-driven arrested phase-separation of polyelectrolyte-gold nanoparticle assemblies leading to plasmonic oligomers

Aggregates of charged metal particles obtained by electrostatic coupling with a compound of opposite charge in the vicinity of the net zero charge ratio are of interest in the field of plasmonics because the inter-particle distance is minimal, which favours plasmonic coupling. However, these structures present a low colloidal stability limiting the development of applications. In this article we show that globally neutral aggregates formed by electrostatic complexation of citrate-stabilized gold particles and a quaternized chitosan (i.e., polycation) around the net zero charge ratio could be stabilized at a nanometric size by the subsequent addition of polyelectrolyte chains. Furthermore, the sign of the charge carried by the stabilizing chains determines the sign of the global charge carried by the stabilized complexes. The stabilization is demonstrated in saline environment on a broad pH range as well as in a cell culture media over periods of several days. Contrarily to stabilization by charged particles, our stabilized complexes are found to retain their initial characteristics (i.e. shape, size, internal structure and optical properties) after stabilization. Hence, the plasmonic coupling allows to maximize the optical absorption around the 800 nm wavelength at which the lasers used for thermoplasmonic and surface enhanced Raman scattering analysis operate.
Survival benefit of combinatorial osimertinib rechallenge and entrectinib in an EGFR-mutant NSCLC patient with acquired LMNA-NTRK1 fusion following osimertinib resistance

Acquired resistance to osimertinib is inevitable and heterogeneous despite its documented efficacy against EGFR-mutated non-small cell lung cancer (NSCLC). Subsequent therapeutic options assume the dominant form of the resistance mechanism; however, the more rare oncogenic driver, NTRK1 fusion, has also reportedly conferred osimertinib resistance. Nevertheless, clear-cut options when NSCLCs are driven by EGFR mutation and the subsequent NTRK fusion are lacking. This is a case of NSCLC wherein exon 19 deletion in EGFR (19del) and acquired LMNA-NTRK1 fusion were accompanied by the persistence of EGFR T790M. The patient underwent peritoneal metastasis after multiple targeted therapies: gefitinib, osimertinib, chemotherapy, and anlotinib plus docetaxel (in clinical trials). Osimertinib was subsequently re-administered with the NTRK fusion inhibitor entrectinib, resulting in remission of peritoneal metastases even after slow progression of pancreatic metastasis over the following 5 months. An extensive literature review to identify the efficacies of therapies for NTRK fusion as the means to acquired resistance to EGFR TKIs revealed that blocking both the EGFR mutation and the subsequent NTRK fusion can provide clinical benefits following EGFR TKIs resistance; however, the efficacy and safety of combination therapies must be further investigated. To precisely manage EGFR-mutated NSCLCs, it is also essential to identify the resistance mechanisms by repeating biopsies.
Application of a new integrated sediment quality assessment method to Huelva estuary and its littoral of influence (Southwestern Spain)

A new integrated sediment quality assessment method composed of several assays (particle size profile, total metal content, protease K extraction, total organic carbon, toxicity bioassay with Photobacterium phosphoreum and macrobenthic community alteration) that provides a single result, the environmental degradation index (EDI), has been developed. The new method was tested on the Huelva estuary (southwest of Spain), a highly polluted area where metals dissolved in the water of the Tinto and Odiel rivers precipitate after flowing through the Iberian Pyrite Belt, one of the largest metallogenic areas of massive sulphide deposits in the world. The proposed method satisfactorily was able to reflect different degrees of pollution on the environmental degradation index. Thus, EDI categorized littoral samples as slightly degraded and all the Tinto and some of the Odiel as very highly degraded, emphasizing the lower zone of the Tinto estuary as the most deeply degraded of the entire study area.
Centroidal Voronoi Tessellations- A New Approach to Random Testing

Although Random Testing (RT) is low cost and straightforward, its effectiveness is not satisfactory. To increase the effectiveness of RT, researchers have developed Adaptive Random Testing (ART) and Quasi-Random Testing (QRT) methods which attempt to maximize the test case coverage of the input domain. This paper proposes the use of Centroidal Voronoi Tessellations (CVT) to address this problem. Accordingly, a test case generation method, namely, Random Border CVT (RBCVT), is proposed which can enhance the previous RT methods to improve their coverage of the input space. The generated test cases by the other methods act as the input to the RBCVT algorithm and the output is an improved set of test cases. Therefore, RBCVT is not an independent method and is considered as an add-on to the previous methods. An extensive simulation study and a mutant-based software testing investigation have been performed to demonstrate the effectiveness of RBCVT against the ART and QRT methods. Results from the experimental frameworks demonstrate that RBCVT outperforms previous methods. In addition, a novel search algorithm has been incorporated into RBCVT reducing the order of computational complexity of the new approach. To further analyze the RBCVT method, randomness analysis was undertaken demonstrating that RBCVT has the same characteristics as ART methods in this regard.
Keeping Watch on Intangible Cultural Heritage: Live Transmission and Sustainable Development of Chinese Lacquer Art

Countries all over the world have been constantly exploring ways to rescue and protect intangible cultural heritage. While learning from other countries' protection measures, the Chinese government is also constantly exploring ways that conform to China's national conditions. As China's first batch of intangible cultural heritage, lacquer art boasts a brilliant history, but many people are not familiar with it today. Moreover, in the process of modernization, the lacquer art transmission is declining day by day, and it is facing unprecedented major crises such as loss and division of history into periods. Hence, it is essential to verify and reveal the challenges and dilemmas in the lacquer art transmission, and come up with corresponding protection measures around these problems. First of all, this research, through literature review, horizontally explores the current research status and the universal problems of lacquer art transmission from the macro level. With a view to make up for the deficiencies of the existing research and further supplement the empirical evidence, the current research, with the transmission of Chengdu lacquer art as an example and through in-depth interviews, tracks and investigates the whole process of transmission of Chengdu Lacquer Art Training Institute, and vertically analyzes the survival situation of lacquer art transmission and the core problems affecting transmission behaviors from the micro level. In the final conclusion, the research comes up with corresponding countermeasures and suggestions for the identified key problems, which is of significant reference value for facilitating the live transmission and sustainable development of Chinese lacquer art.
A 36 mu W 2.8-3.4 dB Noise Figure Impedance Boosted and Noise Attenuated LNA for NB-IoT

This paper describes an ultra-low-power low-noise amplifier (LNA) targeting the 617-652 MHz narrowband-IoT (NB-IoT) frequency band. The design breaks the trade-off in practice between the input matching, minimum required DC current, and minimum NF by using a step-down transformer with an equivalent turns ratio of less than 1, resulting in input impedance boosting and noise attenuation of the main transistor. A local feedback loop is also employed to use the LNA current source transconductance to further attenuate the main transistor noise without additional power cost. The LNA power consumption scales efficiently with the required overall transconductance without being limited to the input matching condition, in practice, while enabling a sub-3dB minimum NF, making it a suitable option for a wide range of applications. A comparison between the proposed, conventional, and state-of-the-art low-power LNA structures is presented with both analytical and simulation models, while measurement results of a prototype designed in a 65 nm technology show a sub-3dB minimum noise figure (NF), -8.8 dBm IIP3, 15 dB gain, and a state-of-the-art FoM of 39.74 dB while consuming only 36 mu W from a 0.55 V supply.
An Educational Needs Assessment for Outpatient Palliative Care Clinicians

Introduction: As the field of palliative medicine continues to grow in community-based settings, outpatient palliative care clinics have become an important site for providing upstream palliative care to patients and families. It is unclear whether current training models, focused predominantly on the inpatient setting, adequately prepare clinicians for outpatient palliative care practice. Methods: We performed an online educational needs assessment survey of physicians and advanced practice providers working in outpatient palliative care clinics. Survey questions focused on the importance of specific palliative care knowledge, skills, and attitudes in outpatient practice using the Accreditation Council of Graduate Medical Education Hospice and Palliative Medicine (HPM) curricular milestones to guide survey development. We also explored clinician perception of training adequacy and current educational needs relevant to outpatient practice. Results: One hundred sixty-four clinicians, including 122 (74.4%) physicians, 32 (19.5%) nurse practitioners, and 8 (4.9%) physician assistants, completed our survey. Clinicians had a median of 10 years of HPM experience and 6 years of outpatient experience. We identified two main areas of perceived knowledge or skill deficit: navigating insurance and prior authorizations and co-management of pain and opioid use disorder. Conclusion: Addressing gaps in education and preparedness for outpatient practice is essential to improve clinician competence and efficiency as well as patient care, safety, and care coordination. This study identifies practice management and opioid stewardship as potential targets for educational interventions. The development of curricula related to these outpatient skills may improve clinicians' ability to provide safe, patient-centered care with confidence.
Improving Chamfer Template Matching Using Image Segmentation

This letter proposes an effective method to improve object location in Chamfer template matching (CTM) based object detection using image segmentation. In our method, object bounding boxes are iteratively adjusted to fit with the object images obtained from image segmentation in a probabilistic model. The proposed method was tested with state-of-the-art CTM-based object detectors. Experimental results have shown the proposed method improved the location accuracy of the object detectors and reduce the false alarms rate.