ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Planar thinned array design by hybrid analytical-stochastic optimisation

The integration of analytical strategies and global optimisation techniques is proposed to address the limitations of current almost difference sets (ADSs) methods for thinning planar apertures. Three design problems associated to these limitations are formulated and the customisation of the optimisation operators to exploit the a-priori information provided by ADS sequences is presented. A numerical validation, including full-wave simulations and comparisons with state-of-the-art methods, is illustrated.
Genetic resistance to infections in sheep

This paper considers genetic resistance to infectious disease in sheep, with appropriate comparison with goats, and explores how such variation may be used to assist in disease control. Many studies have attempted to quantify the extent to which host animals differ genetically in their resistance to infection or in the disease side-effects of infection, using either recorded animal pedigrees or information from genetic markers to quantify the genetic variation. Across all livestock species, whenever studies are sufficiently well powered, then genetic variation in disease resistance is usually seen and such evidence is presented here for three infections or diseases of importance to sheep, namely mastitis, foot rot and scrapie. A further class of diseases of importance in most small ruminant production systems, gastrointestinal nematode infections, is outside the scope of this review. Existence of genetic variation implies the opportunity, at least in principle, to select animals for increased resistance, with such selection ideally used as part of an integrated control strategy. For each of the diseases under consideration, evidence for genetic variation is presented, the role of selection as an aid to disease control is outlined and possible side effects of selection in terms of effects in performance, effects on resistance to other diseases and potential parasite/pathogen coevolution risks are considered. In all cases, the conclusion is drawn that selection should work and it should be beneficial, with the main challenge being to define cost effective selection protocols that are attractive to sheep farmers.
Multi-Source Multi-Destination Hybrid Infrastructure-Aided Traffic Aware Routing in V2V/I Networks

The concept of the "connected car " offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving. However, in urban Vehicular Networks (VNs), the high mobility of vehicles along roads poses major challenges to the routing protocols needed for a reliable and flexible vehicular communications system. Thus, urban VNs rely on static Road-Side-Units (RSUs) to forward data and to extend coverage across the network. In this paper, we first propose a new Q-learning-based routing algorithm, namely Infrastructure-aided Traffic-Aware Routing (I-TAR), which leverages the static wired RSU infrastructure for packet forwarding. Then, we focus on the multi-source, multi-destination problem and the effect this imposes on node availability, as nodes also participate in other communications paths. This motivates our new hybrid approach, namely Hybrid Infrastructure-aided Traffic Aware Routing (HI-TAR) that aims to select the best Vehicle-to-Vehicle/Infrastructure (V2V/I) route. Our findings demonstrate that I-TAR can achieve up to 19% higher average packet-delivery-ratio (APDR) compared to the state-of-the-art. Under a more realistic scenario, where node availability is considered, a decline of up to 51% in APDR performance is observed, whereas the proposed HI-TAR in turn can increase the APDR performance by up to 50% compared to both I-TAR and the state-of-the-art. Finally, when multiple source-destination vehicle pairs are considered, all the schemes that model and consider node availability, i.e. limited-availability, achieve from 72.2% to 82.3% lower APDR, when compared to those that do not, i.e. assuming full-availability. However, HI-TAR still provides 34.6% better APDR performance than I-TAR, and similar to 40% more than the state-of-the-art.
Digital camera identification by fingerprint's compact representation

In this paper we deal with the issue of digital camera identification (DCI) based on images. This area matches the digital forensics (DF) research. This topic has attracted many researchers and number of algorithms for DCI have been proposed. However, majority of them focus only on camera identification with high accuracy without taking into account the speed of image processing. In this paper we propose an effective algorithm for much faster camera identification than state-of-the-art algorithms. Experimental evaluation conducted on two large image datasets including almost 14.000 images confirms that the proposed algorithm achieves high classification accuracy of 97 [%] in much shorter time compared with state-of-the-art algorithms which obtained 92.0 - 96.0 [%]. We also perform a statistical analysis of obtained results which confirms their reliability.
A Screening of Antimalarials Extends the Range of Known Escherichia coli AcrB Efflux Substrates and Reveals Two Candidates with Antimicrobial Drug-Enhancing Activity

Efflux by resistance nodulation cell division transporters, such as AcrAB-TolC in Escherichia coli, substantially contributes to the development of Gram-negative multidrug resistance. Therefore, the finding of compounds that counteract efflux is an urgent goal in the fight against infectious diseases. Previously, an efflux inhibitory activity of the antimalarials mefloquine and artesunate was reported. In this study, we have investigated further antimalarials regarding efflux by AcrB, the pumping part of AcrAB-TolC, and their drug-enhancing potency in E. coli. We show that 10 of the 24 drugs tested are substrates of the multidrug efflux pump AcrB. Among them, tafenoquine and proguanil, when used at subinhibitory concentrations, caused an at least 4- and up to 24-fold enhancement in susceptibility to 6 and 14 antimicrobial agents, respectively. Both antimalarials are able to increase the intracellular accumulation of Hoechst 33342, with proguanil showing similar effectiveness as the efflux inhibitor 1-(1-naphthylmethyl)piperazine. In the case of proguanil, AcrB-dependent efflux inhibition could also be demonstrated in a real-time efflux assay. In addition to presenting new AcrB substrates, our study reveals two previously unknown efflux inhibitors among antimalarials. Particularly proguanil appears as a promising candidate and its chemical scaffold might be further optimized for repurposing as antimicrobial drug enhancer.
Allelic variation in the Arabidopsis TNL CHS3/CSA1 immune receptor pair reveals two functional cell-death regulatory modes

Some plant NLR immune receptors are encoded in head-to-head "sensor-executor" pairs that function together. Alleles of the NLR pair CHS3/CSA1 form three clades. The clade 1 sensor CHS3 contains an integrated domain (ID) with homology to regulatory domains, which is lacking in clades 2 and 3. In this study, we defined two cell-death regulatory modes for CHS3/CSA1 pairs. One is mediated by ID domain on clade 1 CHS3, and the other relies on CHS3/CSA1 pairs from all clades detecting perturbation of an associated pattern-recognition receptor (PRR) co-receptor. Our data support the hypothesis that an ancestral Arabidopsis CHS3/CSA1 pair gained a second recognition specificity and regulatory mechanism through ID acquisition while retaining its original specificity as a "guard" against PRR co-receptor perturbation. This likely comes with a cost, since both ID and non-ID alleles of the pair persist in diverse Arabidopsis populations through balancing selection.
Silver nanoparticles from AgNO3-affinin complex synthesized by an ecofriendly route: chitosan-based electrospun composite production

A novel nanofibrous chitosan-based composite containing affinin and silver nanoparticles is obtained by electrospinning. Silver nanoparticles are synthesized by sunlight photoreduction of the metal complex [Ag-2-(affinin)](NO3)(2) in polymeric solution, via a green one-pot methodology, wherein chitosan and affinin act as reducing, dispersing and stabilizing agent.
Investigating the energy-environmental Kuznets curve under panel quantile regression: a global perspective

Energy is regarded as an engine of economic growth and an important ingredient of human survival and development, but it can lead to deterioration of environmental quality. The study investigates the energy environmental Kuznets curve (EEKC) during the 1990-2017 period for 144 countries using models for total energy, renewable energy, and non-renewable energy consumptions. We employ panel mean and quantile regressions, accounting for individual and distributional heterogeneities. It is found that the EEKC sustains among the higher middle-income countries while it cannot be verified at some lower-income quantiles due to the heterogeneous nature of the different groups of countries. The relationship between economic growth, total energy, and non-renewable energy consumption is positive and non-linear. The quantile estimations revealed mixed (positive and non-linear, inverted U-shape, U-shape, and N-shape) EEKC. The maximum and minimum turning values of GDP per capita for total energy consumption (is 43,201.58 and 89,630.49), for renewable energy consumption (53,535.07 and 89,869.41), and for non-renewable energy consumption (42,188.16 and 89,487.71). Urbanization and population growth had positive impacts on energy consumption while these effects become more significant as moving from low to high-income quantiles. The study implies that while the developed nations can adopt energy-efficient policies without compromising on the growth momentum and environment, this might be not recommended for the developing nations and it would be preferable for these countries to "grow first and clean up later." The study indicates the importance of the developed nations to support the developing countries to achieve economic growth along the EEKC by transferring energy-efficient technologies.
Domain Adaptation for Microscopy Imaging

Electron and light microscopy imaging can now deliver high-quality image stacks of neural structures. However, the amount of human annotation effort required to analyze them remains a major bottleneck. While machine learning algorithms can be used to help automate this process, they require training data, which is time-consuming to obtain manually, especially in image stacks. Furthermore, due to changing experimental conditions, successive stacks often exhibit differences that are severe enough to make it difficult to use a classifier trained for a specific one on another. This means that this tedious annotation process has to be repeated for each new stack. In this paper, we present a domain adaptation algorithm that addresses this issue by effectively leveraging labeled examples across different acquisitions and significantly reducing the annotation requirements. Our approach can handle complex, nonlinear image feature transformations and scales to large microscopy datasets that often involve high-dimensional feature spaces and large 3D data volumes. We evaluate our approach on four challenging electron and light microscopy applications that exhibit very different image modalities and where annotation is very costly. Across all applications we achieve a significant improvement over the state-of-the-art machine learning methods and demonstrate our ability to greatly reduce human annotation effort.
Breathing is coupled with voluntary initiation of mental imagery

Previous research has suggested that bodily signals from internal organs are associated with diverse cortical and subcortical processes involved in sensory-motor functions, beyond homeostatic reflexes. For instance, a recent study demonstrated that the preparation and execution of voluntary actions, as well as its underlying neural activity, are coupled with the breathing cycle. In the current study, we investigated whether such breathing-action coupling is limited to voluntary motor action or whether it is also present for mental actions not involving any overt bodily movement. To answer this question, we recorded electroencephalography (EEG), electromyography (EMG), and respiratory signals while participants were conducting a voluntary action paradigm including self-initiated motor execution (ME), motor imagery (MI), and visual imagery (VI) tasks. We observed that the voluntary initiation of ME, MI, and VI are similarly coupled with the respiration phase. In addition, EEG analysis revealed the existence of readiness potential (RP) waveforms in all three tasks (i.e., ME, MI, VI), as well as a coupling between the RP amplitude and the respiratory phase. Our findings show that the voluntary initiation of both imagined and overt action is coupled with respiration, and further suggest that the breathing system is involved in preparatory processes of voluntary action by contributing to the temporal decision of when to initiate the action plan, regardless of whether this culminates in overt movements.
Isolation of a diphenylamine-degrading bacterium and characterization of its metabolic capacities, bioremediation and bioaugmentation potential

The antioxidant diphenylamine (DPA) is used in fruit-packaging plants for the control of the physiological disorder apple scald. Its use results in the production of DPA-contaminated wastewater which should be treated before finally discharged. Biological treatment systems using tailored-made microbial inocula with specific catabolic activities comprise an appealing and sustainable solution. This study aimed to isolate DPA-degrading bacteria, identify the metabolic pathway of DPA and evaluate their potential for future implementation in bioremediation and biodepuration applications. A Pseudomonas putida strain named DPA1 able to rapidly degrade and utilize DPA as the sole C and N source was enriched from a DPA-contaminated soil. The isolated strain degraded spillage-level concentrations of DPA in liquid culture (2000 mg L(-1)) and in contaminated soil (1000 mg kg(-1)) and metabolized DPA via the transient formation of aniline and catechol. Further evidence for the bioremediation and biodepuration potential of the P. putida strain DPA1 was provided by its capacity to degrade the post-harvest fungicide ortho-phenylphenol (OPP), concurrently used by the fruit-packaging plants, although at slower rates and DPA in a wide range of pH (4.5-9) and temperatures (15-37 °C). These findings revealed the high potential of the P. putida strain DPA1 for use in future soil bioremediation strategies and/or as start-up inocula in wastewater biodepuration systems.
Estimating the selectivity of LIKE queries using pattern-based histograms

Accurate cost and time estimation of a query is one of the major success indicators for database management systems. SQL allows the expression of flexible queries on text-formatted data. The LIKE operator is used to search for a specified pattern (e.g., LIKE "luck%") in a string database. It is vital to estimate the selectivity of such flexible predicates for the query optimizer to choose an efficient execution plan. In this paper, we study the problem of estimating the selectivity of a LIKE query predicate over a bag of strings. We propose a new type of pattern-based histogram structure to summarize the data distribution in a particular column. More specifically, we first mine sequential patterns over a given string database and then construct a special histogram out of the mined patterns. During query optimization time, pattern-based histograms are exploited to estimate the selectivity of a LIKE predicate. The experimental results on a real dataset from DBLP show that the proposed technique outperforms the state of the art for generic LIKE queries like %s(1)%s(2)%...%s(n) % where s(i) represents one or more characters. What is more, the proposed histogram structure requires more than two orders of magnitude smaller memory space, and the estimation time is almost an order of magnitude less in comparison to the state of the art.
Confined Excitons in Spherical-Like Halide Perovskite Quantum Dots

Quantum dots (QDs) offer unique physical properties and novel application possibilities like single-photon emitters for quantum technologies. While strongly confined III-V and II-VI QDs have been studied extensively, their complex valence band structure often limits clear observations of individual transitions. In recently emerged lead-halide perovskites, band degeneracies are absent around the bandgap reducing the complexity of optical spectra. We show that for spherical-like CsPbBr3 QDs with diameters >6 nm, excitons confine with respect to their center-of-mass motion leading to well-pronounced resonances in their absorption spectra. Optical pumping of the lowest-confined exciton with femtosecond laser pulses not only bleaches all excitons but also reveals a series of distinct induced absorption resonances which we attribute to exciton-to-biexciton transitions and are red-shifted by the biexciton binding energy (∼40 meV). The temporal dynamics of the bleached excitons further support our exciton confinement model. Our study provides the first insight into confined excitons in CsPbBr3 QDs and gives a detailed understanding of their linear and nonlinear optical spectra.
Prospects of Botanical Compounds and Pesticides as Sustainable Management Strategies Against Spodoptera frugiperda

Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) (fall armyworm) is an extremely destructive insect pest that causes crop losses, especially cereal production across the world. Its management is challenged by its high migratory ability, polyphagous nature, high fecundity level, and short life cycle. It has become a serious threat across the globe that requires proactive and coordinated regional and global interventions. Although synthetic insecticides have been widely utilized to control the pest, there are numerous inherent challenges associated with the overreliance and overuse of these chemicals, e.g., toxicity to humans, destruction of natural pest enemies and pollinators, environmental and food contamination, pest resurgence, secondary pest outbreaks, and resistance development. Plant-derived pesticides such as Azadirachta indica, Eucalyptus globulus, Jatropha curcas, Lantana camara, Phytolacca dodecandra, and Piper guineense have been evaluated under laboratory, greenhouse, and field conditions to control S. frugiperda. We are certain that the substantial potential of these plants under field conditions could be enhanced and promoted together with existing plant-based products (registered) for use against S. frugiperda as an alternative in integrated pest management schemes. Therefore, this review highlights challenges and prospects that will help refocus and increase research attention on the development and application of botanical pesticides under field conditions rather than only under laboratory and control conditions to increase the commercialization and adoption rate of this technology across the globe.
Artificial Intelligence in Acute Kidney Injury Prediction

The use of artificial intelligence (AI) in nephrology and its associated clinical research is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.
Occluded thermal face recognition using BoCNN and radial derivative Gaussian feature descriptor

In this work, we propose a Radial derivative Gaussian feature (RDGF) descriptor, a novel handcrafted feature descriptor for disguised thermal face recognition. The feature encoding has been done so that the performance is least affected by noise and works well over challenging datasets. We propose a cascaded framework that combines two modules, namely BoCNN and the RDGF descriptor. The cascading architecture estimates the performance of BoCNN before classification. It also uses a dynamic classifier selector in run time to choose between handcrafted features and the CNN framework to enhance the overall performance. We also propose a thermal face dataset with partial occlusion. We have compared the performance of the RDGF descriptor with state-of-the-art descriptors on the IIIT-Delhi disguised thermal face dataset and our proposed dataset. RDGF exhibits better performance compared to other state-of-the-art descriptors. Our proposed descriptor shows relative increment of 56.84%, 64.92%, 67.25%, 64.03%, 48.06%, and 7.28% on IIIT-D Occluded Thermal Dataset when compared with LBP, LDP, LBDP, LVP, LGHP, and HOG, respectively. A similar enhancement of accuracy has been observed on our proposed dataset as well. An exhaustive comparison based on the performance of the cascaded framework with state-of-the-art CNN models has also been done in a similar fashion. (c) 2023 Elsevier B.V. All rights reserved.
Sketch Recognition: What Lies Ahead?

What is the state-of-the-art in sketch recognition and what are some important future research directions in matching sketches with digital face images? This opinion paper focuses on answering these questions through proposing three important steps that need to move the field forward: (i) create a large, real world forensic sketch database, (ii) develop fundamental understanding of human cognition of processing sketches, and (iii) develop improved algorithms for matching sketches with mugshot photos. (C) 2016 Elsevier B.V. All rights reserved.
Citric acid can enhance the uptake and accumulation of organophosphate esters (OPEs) in Suaeda salsa rhizosphere: Potential for phytoremediation

Bioaccumulation of organophosphate esters (OPEs) by plants has been widely studied, but how root exudates influence their bioavailability to plants is poorly understood. Here, we examined whether root exudates could promote desorption of OPEs, thereby enhancing bioavailability and subsequent accumulation potential. Root exudate components exert great influences on the sorption/desorption isotherms of OPEs in soils, resulting in activating OPEs and enhanced bioavailability. Among root exudate components, citric acid was confirmed to play a crucial role in driving OPEs, with 77.7-90.3 % attribution. Citric acid at rhizosphere levels (0.01-0.4 mM) can successfully reduce OPEs sorption to soils by decreasing electrostatic interaction, ligand exchange, and hydrophobic force. Pot experiments indicated that the addition of citric acid can significantly increase OPEs dissolution and bioaccumulation from the rhizosphere soil to Suaeda salsa. A higher level of citric acid in rhizosphere soil resulted in a higher accumulation of OPEs in Suaeda salsa, which was partly attributed to the enhanced OPEs mobility, and the increased root lengths (13.4-29.0 %) and tip numbers (60.2-120 %), promoting OPEs uptake by roots. Our findings suggest the activation process of OPEs in soils by citric acid at rhizosphere levels and provide insights into designing LMWOAs-enhanced phytoremediation techniques in natural environment.