ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification

Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and BALO algorithms to tackle the imbalanced classification problem, delivering different cost weights to different classes of samples. In our method, the BALO algorithm determines the cost weights and extract more significant features to improve the classification performance. Experiments conducted on eleven imbalanced data sets have shown that the CFGVM algorithm significantly improves the classification performance of minority class samples. By comparing with similar algorithms and state-of-the-art algorithms, the proposed algorithm significantly outperforms in performance and produces better classification results.
Pyrrole-2-carbaldehydes with neuroprotective activities from Moringa oleifera seeds

Seven undescribed pyrrole-2-carbaldehydes (pyrrolemorines A-G), along with four known analogs, were isolated from the aqueous extract of Moringa oleifera seeds. The structures were elucidated by comprehensive spectroscopic and chemical analyses using HRMS and NMR spectra, acid hydrolysis, and Rh2(OCOCF3)4-induced ECD experiments. Pyrrolemorines A, E, and pyrrolemarumine displayed neuroprotective activities against oxygen-glucose deprivation/reperfusion injury in PC12 cells by regulating NF-κb and Nrf2.
High Dielectric Transparent Film Tailored by Acceptor and Donor Codoping

High dielectric constant materials are of particular current interests as indispensable components in transistors, capacitors, etc. In this context, there are emerging trends to exploit defect engineering in dielectric ceramics for enhancing the performance. However, demonstrations of similar high dielectric performance in integration-compatible crystalline films are rare. Herein, such a breakthrough via the functionalization of donor-acceptor dipoles by compositional tuning in GaCu codoped ZnO films is reported. The dielectric constant reaches ~200 at 1 kHz and the optical transmittance in visible light reaches ~80%. Importantly, by analyzing the impedance spectroscopy data, prominent relaxation mechanisms in correlation with the dipole properties, enabling consistent explanations of the dielectric constant as a function of frequency are discriminated. The atomistic nature of the dipoles is revealed by the systematic X-ray spectroscopy analysis. Spectacularly, similar trends for the dielectric properties are observed, while synthesizing samples by pulsed laser deposition and ion implantation, indicating the general character of the phenomena.
SSEM: A Novel Self-Adaptive Stacking Ensemble Model for Classification

In the past decades, the ensemble systems have been shown as an efficient method to increase the accuracy and stability of classification algorithms. However, how to get a valid combination of multiple base-classifiers is still an open question to be solved. In this paper, based on the genetic algorithm, a new self-adaptive stacking ensemble model (called SSEM) is proposed. Different from other ensemble learning classification algorithms, SSEM selectively integrates different base-classifiers, and automatically selects the optimal base-classifier combination and hyper-parameters of base-classifiers via the genetic algorithm. It is noted that all of machine learning methods can be the components of SSEM. In this work, based on two base-classifier selection principles (low complexity of base-classifier and high diversity between different base-classifiers), we select five state-of-art classifiers including Naive Bayes (NB), Extremely Randomized trees (ERT), Logistic, Random Forest (RF) and Classification and Regression Tree (CART) as the base-classifiers of SSEM. To demonstrate the efficiency of SSEM, we have applied it to nine different datasets. Compared with other 11 state-of-art classifiers (NB, ERT, Logistic, RF, CART, Back Propagation Network (BPN), Support Vector Machine (SVM), AdaBoost, Bagging, Convolutional Neural Networks (CNN) and Deep neural network (DNN)), SSEM always performs the best under the five evaluation indexes (Accuracy, Recall, AUC, F1-score and Matthews correlation coefficient (MCC)). Moreover, the significance test result shows that SSEM can achieve highly competitive performance against the other 11 state-of-art classifiers. Altogether, it is evident that SSEM can be a useful framework for classification.
Fractional Order PID Design based on Novel Improved Slime Mould Algorithm

This study attempts to maintain the terminal voltage level of an automatic voltage regulator (AVR) and control the speed of a direct current (DC) motor using a fractional order proportional integral derivative (FOPID) controller. The best parameters of the controller have been adjusted using a novel meta-heuristic algorithm called opposition-based hybrid slime mold with simulated annealing algorithm. The proposed algorithm aims to improve the original slime mold algorithm in terms of exploitation and exploration using simulated annealing and opposition-based learning, respectively. A time domain objective function was adopted as performance index to design the FOPID-based AVR and DC motor systems. The initial performance evaluation was carried out using unimodal and multimodal benchmark functions. The results confirmed the superior exploration and exploitation capabilities of the developed algorithm compared to the other state-of-the-art optimization algorithms. The performance of the proposed algorithm has also been assessed through statistical tests, time domain and frequency domain simulations along with robustness and disturbance rejection analyses for both DC motor and AVR systems. The proposed algorithm has shown superior capabilities for the respective systems compared to the other state-of-the-art optimization algorithms used for the same purpose.
Guggulsterone Attenuated Lipopolysaccharide-Induced Inflammatory Responses in Mouse Inner Medullary Collecting Duct-3 Cells

Guggulsterone (GS) is a phytosterol that has been used to treat inflammatory diseases such as colitis, obesity, and thrombosis. Although many previous studies have examined activities of GS, the effect of GS on lipopolysaccharide (LPS)-induced inflammatory responses in mouse inner medullary collecting duct-3 (mIMCD-3) cells have not been examined. Therefore, here, we investigated the anti-inflammatory action of GS on mIMCD-3 cells exposed to LPS. LPS treatment on mIMCD-3 cells produced pro-inflammatory molecules such as inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) significantly; however, GS treatment significantly inhibited the production of pro-inflammatory molecules. In addition, GS inhibited the degradation of Iκ-Bα and translocation of NF-κB on mIMCD-3 cells. These results suggest that GS could inhibit inflammatory responses in collecting duct cells which could contribute to kidney injury during systemic infection.
Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks

Multi-stage learning is an effective technique for invoking multiple deep-learning modules sequentially. This paper applies multi-stage learning to speech enhancement by using a multi-stage structure, where each stage comprises a self-attention (SA) block followed by stacks of temporal convolutional network (TCN) blocks with doubling dilation factors. Each stage generates a prediction that is refined in a subsequent stage. A feature fusion block is inserted at the input of later stages to re-inject original information. The resulting multi-stage speech enhancement system, multi-stage SA-TCN, is compared with state-of-the-art deep-learning speech enhancement methods using the LibriSpeech and VCTK datasets. The multi-stage SA-TCN system's hyperparameters are fine-tuned, and the impact of the SA block, the feature fusion block, and the number of stages are determined. The use of a multi-stage SA-TCN system as a front-end for automatic speech recognition systems is also investigated. It is shown that the multi-stage SA-TCN systems perform well relative to other state-of-the-art systems in terms of speech enhancement and speech recognition scores.
Pseudomonas aeruginosa in the Cystic Fibrosis Lung

Cystic fibrosis is a common genetically inherited, multisystem disorder caused by loss of function of the cystic fibrosis transmembrane conductance regulator (CFTR) protein, an apically situated anion channel. In the lung, lack of CFTR leads to airway surface dehydration, mucociliary clearance failure and an acidic pH in which innate defence molecules are rendered ineffective. Infection occurs early in life, with P. aeruginosa dominating by adolescence. The characteristic features of the CF airway highlighted above encourage persistence of infection, but P. aeruginosa also possess an array of mechanisms with which they attack host defences and render themselves protected from antimicrobials. Early eradication is usually successful, but this is usually transient. Chronic infection is manifest by biofilm formation which is resistant to treatment. Outcomes for people with CF have improved greatly in the last few decades, but particularly so with the recent advent of small molecule CFTR modulators. However, despite impressive efficacy on lung function and exacerbation frequency, most people with chronic infection remain with their pathogens. There is an active pipeline of new treatments including anti-biofilm and anti-quorum sensing molecules and non-drug approaches such as bacteriophage. Studies are reviewed and challenges for future drug development considered.
Whole-blood immunoassay for gamma H2AX as a radiation biodosimetry assay with minimal sample preparation

The current state of the art in high-throughput minimally invasive radiation biodosimetry involves the collection of samples in the field and analysis at a centralized facility. We have developed a simple biological immunoassay for radiation exposure that could extend this analysis out of the laboratory into the field. Such a forward placed assay would facilitate triage of a potentially exposed population. The phosphorylation and localization of the histone H2AX at double-stranded DNA breaks has already been proven to be an adequate surrogate assay for reporting DNA damage proportional to radiation dose. Here, we develop an assay for phosphorylated H2AX directed against minimally processed sample lysates. We conduct preliminary verification of H2AX phosphorylation using irradiated mouse embryo fibroblast cultures. Additional dosimetry is performed using human blood samples irradiated ex vivo. The assay reports H2AX phosphorylation in human blood samples in response to ionizing radiation over a range of 0-5 Gy in a linear fashion, without requiring filtering, enrichment, or purification of the blood sample.
Characterizing the Rate-Memory Tradeoff in Cache Networks Within a Factor of 2

We consider a basic caching system, where a single server with a database of N files (e.g., movies) is connected to a set of K users through a shared bottleneck link. Each user has a local cache memory with a size of M files. The system operates in two phases: a placement phase, where each cache memory is populated up to its size from the database, and a following delivery phase, where each user requests a file from the database, and the server is responsible for delivering the requested contents. The objective is to design the two phases to minimize the load (peak or average) of the bottleneck link. We characterize the rate-memory tradeoff of the above caching system within a factor of 2.00884 for both the peak rate and the average rate (under uniform file popularity), improving the state of the arts that are within a factor of 4 and 4.7, respectively. Moreover, in a practically important case where the number of files (N) is large, we exactly characterize the tradeoff for systems with no more than five users and characterize the tradeoff within a factor of 2 otherwise. To establish these results, we develop two new converse bounds that improve over the state of the art.
A dynamic conceptualization of power for sustainability research

This paper takes up the challenge of providing a conceptual power framework to be used in the context of sustainability research. First, challenges of sustainability research are discussed by focusing specifically on recent insights from Integrated Sustainability Assessment (ISA), and on that basis some requirements for concepts to be used in sustainability research are postulated. It is argued that two of the most important aspects of sustainability assessment research are the long-term dynamics of change and an interdisciplinary paradigm. Second, a dynamic power framework is presented that was developed in the context of research on socio-technical sustainability transitions, including the basics of this power framework as well as some empirical illustrations. Third, it is discussed how the presented power framework deals with time, change and long-term dynamics, and how this contributes to the state-of-the-art. Fourth, it is indicated how the power framework integrates interdisciplinary and 'interparadigmaticatic' research requirements, and how this contributes to the state-of-the art. In conclusion, the arguments are summarized and some challenges for future research are distilled. (C) 2010 Elsevier Ltd. All rights reserved.
Region-based multimodal image fusion using ICA bases

In this paper, we present a novel multimodal image fusion algorithm in the independent component analysis (ICA) domain. Region-based fusion of ICA coefficients is implemented, where segmentation is performed in the spatial domain and ICA coefficients from separate regions are fused separately. The ICA coefficients from given regions are consequently weighted using the Piella fusion metric in order to maximize the quality of the fused image. The proposed method exhibits significantly higher performance than the basic ICA algorithm and also shows improvement over other state-of-the-art algorithms.
Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices

Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available.
Can the KG1 cell line be used as a model of dendritic cells and discriminate the sensitising potential of chemicals?

The KG1 myeloid leukaemia was used as source of dendritic cells (DC) to discriminate between respiratory and contact sensitising chemicals. A cocktail of cytokines was used to differentiate KG1 to dendritic like cells (termed dKG1) and the effects of nine chemicals (respiratory and contact sensitisers) and an irritant control on surface marker expression, 'antigen presenting' function and cytokine expression investigated. The stability of these chemicals when dissolved was characterised using MALDI ToF MS. A Hill plot model was used with the cellular viability data to quantify the lethal dose 50% (LD50) and a maximum sub toxic concentration of each chemical defined. Cytokine expression by the treated dKG1 was quantified using multiplex immunobead analysis. Whilst dKG1 cells were morphologically similar to DCs, expression of specific surface markers was not typical for DCs derived from healthy precursor cells. When the chemicals were applied at defined sub toxic doses no effects on dKG1 phenotype, function, or cytokine expression, attributable to the sensitisation properties were discriminated. However, dKG1 cells were much more sensitive to the toxic effects of these chemicals compared to the parent KG1 cells. Only 4 of the 9 chemicals tested were stable when dissolved indicating that the effect of sensitising chemicals on antigen presenting cells may be related to species other than the parent compound.
Printed Motes for IoT Wireless Networks: State of the Art, Challenges, and Outlooks

Although wireless sensor networks (WSNs) have been an active field of research for many years, the modules incorporated by WSN nodes have been mainly manufactured utilizing conventional fabrication techniques that are mostly subtractive, requiring significant amounts of materials and increased chemical waste. The new era of the Internet of Things (IoT) will see the fabrication of numerous small form factor devices for wireless sensing for a plurality of applications, including security, health, and environmental monitoring. The large volume of these devices will require new directions in terms of manufacturing cost and energy efficiency, which will be achieved with redesigned, energy-aware modules. This paper presents the state of the art of printed passives, sensors, energy harvesting modules, actives, and communication front ends, and summarizes the challenges of implementing modules that feature low power consumptions without compromising the low fabrication cost. The plethora of the modules presented herein will facilitate the implementation of low cost, additively manufactured, energy-aware IoT nodes that can be fabricated in large volumes with green processes.
Conceptualizing the State of the Art of Corporate Social Responsibility (CSR) in Green Construction and Its Nexus to Sustainable Development

The study has been investigated on conceptualizing the state of the art of cooperate social responsibility (CSR) in green construction and its nexus to sustainable development. The research objective is to find out the significant relationship between CSR and green construction and further link green construction with sustainable development. The data has been collected from the 319 respondents working on different projects in the construction industry of China. The study is quantitative by nature. SEM analysis with the help of Smart-PLS has been applied to test the hypothesis relationship and mediations between components of CSR, green construction, and sustainable development. Results define that green procurement as a component of green construction strongly mediates between corporate social responsibility and sustainable development, and green design and CO2 emission moderately mediates between corporate social responsibility and sustainable development. This research would add values, benefits, and knowledge toward reducing environmental temperature with the help of green construction occupying the effects of CSR on it. It would be helpful to change the trends in the construction industry to make environmental health protective and to boost the sustainable development.
Concepts and implementation of Spatial Division Multiplexing for guaranteed throughput in Networks-on-Chip

To ensure low power consumption while maintaining flexibility and performance, future Systems-on-Chip (SoC) will combine several types of processor cores and data memory units of widely different sizes. To interconnect the IPs of these heterogeneous platforms, Networks- on- Chip ( NoC) have been proposed as an efficient and scalable alternative to shared buses. NoCs can provide throughput and latency guarantees by establishing virtual circuits between source and destination. State- of- the- art NoCs currently exploit Time Division Multiplexing (TDM) to share network resources among virtual circuits, but this typically results in high network area and energy overhead with long circuit setup time. We propose an alternative solution based on Spatial Division Multiplexing (SDM). This paper describes our design of an SDM- based network, discusses design alternatives for network implementation, and shows why SDM can be better adapted to NoCs than TDM in a specific context. Our case study clearly illustrates the advantages of our technique over TDM in terms of energy consumption, area overhead, and flexibility. A comparison is also performed with a State- of- the- Art industrial reference NoC: Arteris.
Radio frequency ablation for the intrauterine treatment of giant placental chorioangioma associated with fetal compromise: A case report

Giant placental chorioangiomas associated with fetal hyperdynamic circulation complications are rare to see. Here, we summarized a case of giant placental chorioangioma associated with fetal anemia and heart failure treated by radiofrequency ablation (RFA) combined with cordocentesis and intrauterine transfusion. The sonographic appearance of the placental chorioangioma was atypical which was isoechoic with unclear boundary. RFA was performed successfully at 27 weeks of gestation, when the chorioangioma has increased to 17.0 × 10.6 × 12.3 cm3 . Unfortunately, intrauterine fetal demise was found on the first day after operation. After induction of labor, it was pathologically confirmed as placental chorioangioma.
Language Accommodations for Limited English Proficient Patients in Rural Health Care

Over 25 million individuals living in America are limited English proficient, many of whom live in rural communities. Adequate language accommodations are critical to providing effective healthcare for these populations. An online questionnaire was delivered to 42 rural facilities in Washington State. It included questions about their demand for language services, modalities of interpretation, translated documentation and barriers to providing accommodations. Fifteen of 42 (35.7%) responded. Spanish, Russian, Vietnamese, Japanese, Ukrainian and Mam were encountered daily. Telephonic and virtual remote interpreter services were the most widely available. Not all facilities had vital documents translated to frequently encountered languages. Challenges to providing language access were reported by nearly all participants. The rural facilities surveyed all encountered LEP patient populations and offered oral interpretation. Overall, these facilities were meeting requirements for providing language accommodation services. Even so, many facilities reported experiencing barriers to providing these services.