ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Lack of ownership of mobile phones could hinder the rollout of mHealth interventions in Africa

Mobile health (mHealth) interventions, which require ownership of mobile phones, are being investigated throughout Africa. We estimate the percentage of individuals who own mobile phones in 33 African countries, identify a relationship between ownership and proximity to a health clinic (HC), and quantify inequities in ownership. We investigate basic mobile phones (BPs) and smartphones (SPs): SPs can connect to the internet, BPs cannot. We use nationally representative data collected in 2017-2018 from 44,224 individuals in Round 7 of the Afrobarometer surveys. We use Bayesian multilevel logistic regression models for our analyses. We find 82% of individuals in 33 countries own mobile phones: 42% BPs and 40% SPs. Individuals who live close to an HC have higher odds of ownership than those who do not (aOR: 1.31, Bayesian 95% highest posterior density [HPD] region: 1.24-1.39). Men, compared with women, have over twice the odds of ownership (aOR: 2.37, 95% HPD region: 1.96-2.84). Urban residents, compared with rural residents, have almost three times the odds (aOR: 2.66, 95% HPD region: 2.22-3.18) and, amongst mobile phone owners, nearly three times the odds of owning an SP (aOR: 2.67, 95% HPD region: 2.33-3.10). Ownership increases with age, peaks in 26-40 year olds, then decreases. Individuals under 30 are more likely to own an SP than a BP, older individuals more likely to own a BP than an SP. Probability of ownership decreases with the Lived Poverty Index; however, some of the poorest individuals own SPs. If the digital devices needed for mHealth interventions are not equally available within the population (which we have found is the current situation), rolling out mHealth interventions in Africa is likely to propagate already existing inequities in access to healthcare.
Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer

A noninvasive computer-aided-theragnosis (CAT) system was developed for the early therapeutic cancer response assessment in patients with locally advanced breast cancer (LABC) treated with neoadjuvant chemotherapy. The proposed CAT system was based on multi-parametric quantitative ultrasound (QUS) spectroscopic methods in conjunction with advanced machine learning techniques. Specifically, a kernel-based metric named maximum mean discrepancy (MMD), a technique for learning from imbalanced data based on random undersampling, and supervised learning were investigated with response-monitoring data from LABC patients. The CAT system was tested on 56 patients using statistical significance tests and leave-one-subject-out classification techniques. Textural features using state-of-the-art local binary patterns (LBP), and gray-scale intensity features were extracted from the spectral parametric maps in the proposed CAT system. The system indicated significant differences in changes between the responding and non-responding patient populations as well as high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. The proposed CAT system achieved an accuracy of 85%, 87%, and 90% on weeks 1, 4 and 8, respectively. The sensitivity and specificity of developed CAT system for the same times was 85%, 95%, 90% and 85%, 85%, 91%, respectively. The proposed CAT system thus establishes a noninvasive framework for monitoring cancer treatment response in tumors using clinical ultrasound imaging in conjunction with machine learning techniques. Such a framework can potentially facilitate the detection of refractory responses in patients to treatment early on during a course of therapy to enable possibly switching to more efficacious treatments.
Amyloid-β induced astrocytosis and astrocyte death: Implication of FoxO3a-Bim-caspase3 death signaling

Astrocytes, the main element of the homeostatic system in the brain, are affected in various neurological conditions including Alzheimer's disease (AD). A common astrocytic reaction in pathological state is known as astrocytosis which is characterized by a specific change in astrocyte shape due to cytoskeletal remodeling, cytokine secretion and cellular proliferation. Astrocytes also undergo apoptosis in various neurological conditions or in response to toxic insults. AD is pathologically characterized by progressive deposition of amyloid-β (Aβ) in senile plaques, intraneuronal neurofibrillary tangles, synaptic dysfunction and neuron death. Astrocytosis and astrocyte death have been reported in AD brain as well as in response to Aβ in vitro. However, how astrocytes undergo both proliferation and death in response to Aβ remains elusive. In this study, we used primary cultures of cortical astrocytes and exposed them to various doses of oligomeric Aβ. We found that cultured astrocytes proliferate and manifest all signs of astrocytosis at a low dose of Aβ. However, at high dose of Aβ the activated astrocytes undergo apoptosis. Astrocytosis was also noticed in vivo in response to Aβ in the rat brain. Next, we investigated the mechanism of astrocyte apoptosis in response to a high dose of Aβ. We found that death of astrocyte induced by Aβ requires a set of molecules that are instrumental for neuron death in response to Aβ. It involves activation of Forkhead transcription factor Foxo3a, induction of its pro-apoptotic target Bim and activation of its downstream molecule, caspase3. Hence, this study demonstrates that the concentration of Aβ decides whether astrocytes do proliferate or undergo apoptosis via a mechanism that is required for neuron death.
Ultra-Narrow Linewidth Photo-Emitters in Polymorphic Selenium Nanoflakes

Photoluminescence (PL) in state-of-the-art 2D materials suffers from narrow spectral coverage, relatively broad linewidths, and poor room-temperature (RT) functionality. The authors report ultra-narrow linewidth photo-emitters (ULPs) across the visible to near-infrared wavelength at RT in polymorphic selenium nanoflakes (SeNFs), synthesized via a hot-pressing strategy. Photo-emitters in NIR exhibit full width at half maximum (Γ) of 330 ± 90 µeV, an order of magnitude narrower than the reported ULPs in 2D materials at 300 K, and decrease to 82 ± 70 µeV at 100 K, with coherence time (τc ) of 21.3 ps. The capping substrate enforced spatial confinement during thermal expansion at 250 °C is believed to trigger a localized crystal symmetry breaking in SeNFs, causing a polymorphic transition from the semiconducting trigonal (t) to quasi-metallic orthorhombic (orth) phase. Fine structure splitting in orth-Se causes degeneracy in defect-associated bright excitons, resulting in ultra-sharp emission. Combined theoretical and experimental findings, an optimal biaxial compressive strain of -0.45% cm-1 in t-Se is uncovered, induced by the coefficient of thermal expansion mismatch at the selenium/sapphire interface, resulting in bandgap widening from 1.74 to 2.23 ± 0.1 eV. This report underpins the underlying correlation between crystal symmetry breaking induced polymorphism and RT ULPs in SeNFs, and their phase change characteristics.
Comparative analysis of machine learning algorithms for prediction of smart grid stability(dagger)

The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.
Chromatic Art Gallery Problem with r-Visibility is NP-Complete

The art gallery problem is to find a set of guards who together can observe every point of the interior of a polygon P. We study a chromatic variant of the problem, where each guard is assigned one of k distinct colors. The chromatic art gallery problem is to find a guard set for P such that no two guards with the same color have overlapping visibility regions. We study the decision version of this problem for orthogonal polygons with r-visibility when the number of colors is k = 2. Here, two points are r-visible if the smallest axis-aligned rectangle containing them lies entirely within the polygon. In this paper, it is shown that determining whether there is an r-visibility guard set for an orthogonal polygon with holes such that no two guards with the same color have overlapping visibility regions is NP-hard when the number of colors is k = 2.
Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach

Kernel support vector machines (SVMs) deliver state-of-the-art results in many real-world nonlinear classification problems, but the computational cost can be quite demanding in order to maintain a large number of support vectors. Linear SVM, on the other hand, is highly scalable to large data but only suited for linearly separable problems. In this paper, we propose a novel approach called low-rank linearized SVMto scale up kernel SVM on limited resources. Our approach transforms a nonlinear SVM to a linear one via an approximate empirical kernel map computed from efficient kernel low-rank decompositions. We theoretically analyze the gap between the solutions of the approximate and optimal rank-k kernel map, which in turn provides guidance on the sampling scheme of the Nystrom approximation. Furthermore, we extend it to a semisupervised metric learning scenario in which partially labeled samples can be exploited to further improve the quality of the low-rank embedding. Our approach inherits rich representability of kernel SVM and high efficiency of linear SVM. Experimental results demonstrate that our approach is more robust and achieves a better tradeoff between model representability and scalability against state-of-the-art algorithms for large-scale SVMs.
Transplant surgeons' perspective on antimicrobial stewardship: Experience with TransQIP

Antimicrobial stewardship programs (ASPs) have been shown to reduce the rates of antimicrobial resistance and improve morbidity and mortality in surgical patients. ASPs have largely been underutilized in solid organ transplant programs, and the current state of ASPs in transplantation is reviewed. Continued implementation of ASPs would likely significantly benefit transplant patients. Furthermore, coupling ASPs with robust programmatic metrics (such as transplant-specific NSQIP) will hopefully lead to improved outcomes including morbidity and mortality of solid organ transplant recipients.
Hierarchical deep belief networks based point process model for keywords spotting in continuous speech

Point process model keyword spotting (KWS) system has attracted considerable attentions in the areas of keyword spotting by its capacity that can generalize from a relatively small numbers of training examples. But unfortunately, the accuracy level of the point process model is not comparable with the state-of-the-art KWS systems because of the poor modeling capacity of the phoneme detector, which are based on Gaussian Mixture Models. In this paper, focus on improving the performance of detector in point process model, we propose an enhanced version of point process model, which is based on hierarchical deep belief networks (DBNs). Hierarchical DBNs are used as the phoneme detector in this system, and they combine the advantages of both the DBN and the hierarchical architecture for capturing complex statistical patterns in speech while overcoming the inherent flaws of conventional hidden Markov models and multilayer layer perceptron. Experiments results on TIMIT database show that the proposed method can yield 2% improvement. Furthermore, in the case when training examples are extremely limited, it can achieve better results over state-of-the-art KWS systems. Copyright (c) 2013 John Wiley & Sons, Ltd.
Achieving Integrated Daylighting and Electric Lighting Systems: Current State of the Art and Needed Research

This paper presents the results of a multi-disciplinary effort to clarify the state of the art and the state of practice, and necessary future research for creating the seamless integration and application of light in buildings, regardless of source, which is purposely modulated to illuminate surfaces and designed in a way that is comfortable, healthy, pleasing, cost-effective, and energy efficient. The authors unwrap the research, tools, and technical gaps preventing the full integration of electric lighting and daylighting with advanced facades through the coordination of lighting and windows research activities. The study and a stakeholder workshop captured current technology readiness levels (TRL), as well as research thrusts and implementation guidelines, and identified research priorities, presenting an analysis of the current landscape of lighting metrics-and which metrics are in the critical path for developing integrated daylighting and electric lighting systems, and their design, installation, and technology guidelines. In addition, the study defined stakeholder coordination, pathways to interoperable technology, and the value of viewing the work of the individual research areas holistically rather than in isolation.
High-PSR LDOs: Variations, Improvements, and Best Compromise

Low-Dropout Regulators (LDOs) are used to power noise sensitive applications. Power Supply Rejection (PSR) is a performance metric that measures the LDO's ability to reject noise. Improving PSR has been the focus of many research groups. However, the state of the art does not recognize the best PSR enhancement schemes and collate them under comparable grounds. Further, the pass transistor's diode connection (through the Gate-Drain capacitance) impacts the PSR, and this effect is not quantified in the state of the art. This research aims to bridge these gaps first by explicating the constitution of a high PSR LDO. Then, the impact of the pass transistor's parasitics on PSR are quantified. Following this, the best state of the art PSR enhancement schemes are analyzed and simulated over a high-PSR core under similar conditions. This study reveals the strengths and limitations of each scheme, which unfolds each technique's applications. Results convey that the LDO Filter yields the best PSR improvement at low frequencies, Series feedback at mid frequencies, and RC filter at high frequencies. Assessment concludes that Series Feedback provides the best compromise with respect to PSR enhancement.
The effect of inflammation management on pH, temperature, and bacterial burden

The aim of this feasibility study was to investigate the impact of inflammation management on wound pH, temperature, and bacterial burden, using the principles of TIME and Wound Bed Preparation. A quantitative non-comparative, prospective, descriptive observational design. Following ethical approval, 26 participants with 27 wounds of varying aetiologies were observed twice weekly for 2 weeks. Wounds were treated with cleansing, repeated sharp debridement, and topical cadexomer iodine. Wound pH (pH indicator strips), temperature (infrared camera), bacterial burden (fluorescence imaging) and size (ruler method) was monitored at each visit. The mean age of all participants was 47 years (SD: 20.3 years), and 79% (n = 19) were male, and most wounds were acute (70%; n = 19) and included surgical and trauma wounds, the remaining (30%; n = 8) were chronic and included vascular ulcers and non-healing surgical wounds. Mean wound duration was 53.88 days (SD: 64.49 days). Over the follow up period, pH values ranged from 6 to 8.7, temperature (centre spot) ranged from 28.4°C to 36.4°C and there was an average 39% reduction in wound size. Inflammation management had a positive effect on pH, temperature, bacterial burden, and wound size. This study demonstrated that it was feasible to practice inflammation management using a structured approach to enhance wound outcomes.
Scale Insects Support Natural Enemies in Both Landscape Trees and Shrubs Below Them

Scale insects are frequently abundant on urban trees. Although scales can worsen tree condition, some tree species tolerate moderate scale densities. Scales are prey for many natural enemies. Therefore, scale-infested trees may conserve natural enemies in their canopies and in nearby plants. We examined if scale-infested oaks-Quercus phellos L.-hosted more natural enemies than scale-uninfested oaks-Q. acutissima Carruth. and Q. lyrata Walter in Raleigh, NC. USA. We also tested if natural enemies were more abundant in holly shrubs (Ilex spp.) planted below scale-infested compared to scale-uninfested oaks. We collected natural enemies from the canopies of both tree types and from holly shrubs planted below these trees. To determine if tree type affected the abundance of natural enemies that passively dispersed to shrubs, we created hanging cup traps to collect arthropods as they fell from trees. To determine if natural enemies became more abundant on shrubs below scale-infested compared to scale-uninfested trees over short time scales, we collected natural enemies from holly shrubs below each tree type at three to six-day intervals. Scale-infested trees hosted more natural enemies than scale-uninfested trees and shrubs below scale-infested trees hosted more natural enemies than shrubs under scale-uninfested trees. Natural enemy abundance in hanging cup traps did not differ by tree type; however, shrubs underneath scale-infested trees accumulated more natural enemies than shrubs under scale-uninfested trees in six to nine days. Tolerating moderate pest densities in urban trees may support natural enemy communities, and thus biological control services, in shrubs below them.
Lysosomal positioning regulates Rab10 phosphorylation at LRRK2+ lysosomes

Genetic variation at the leucine-rich repeat kinase 2 (LRRK2) locus contributes to an enhanced risk of familial and sporadic Parkinson's disease. Previous data have demonstrated that recruitment to various membranes of the endolysosomal system results in LRRK2 activation. However, the mechanism(s) underlying LRRK2 activation at endolysosomal membranes and the cellular consequences of these events are still poorly understood. Here, we directed LRRK2 to lysosomes and early endosomes, triggering both LRRK2 autophosphorylation and phosphorylation of the direct LRRK2 substrates Rab10 and Rab12. However, when directed to the lysosomal membrane, pRab10 was restricted to perinuclear lysosomes, whereas pRab12 was visualized on both peripheral and perinuclear LRRK2+ lysosomes, suggesting that lysosomal positioning provides additional regulation of LRRK2-dependent Rab phosphorylation. Anterograde transport of lysosomes to the cell periphery by increasing the expression of ARL8B and SKIP or by knockdown of JIP4 blocked the recruitment and phosphorylation of Rab10 by LRRK2. The absence of pRab10 from the lysosomal membrane prevented the formation of a lysosomal tubulation and sorting process we previously named LYTL. Conversely, overexpression of RILP resulted in lysosomal clustering within the perinuclear area and increased LRRK2-dependent Rab10 recruitment and phosphorylation. The regulation of Rab10 phosphorylation in the perinuclear area depends on counteracting phosphatases, as the knockdown of phosphatase PPM1H significantly increased pRab10 signal and lysosomal tubulation in the perinuclear region. Our findings suggest that LRRK2 can be activated at multiple cellular membranes, including lysosomes, and that lysosomal positioning further provides the regulation of some Rab substrates likely via differential phosphatase activity or effector protein presence in nearby cellular compartments.
Biased Pressure: Cyclic Reinforcement Learning Model for Intelligent Traffic Signal Control

Existing inefficient traffic signal plans are causing traffic congestions in many urban areas. In recent years, many deep reinforcement learning (RL) methods have been proposed to control traffic signals in real-time by interacting with the environment. However, most of existing state-of-the-art RL methods use complex state definition and reward functions and/or neglect the real-world constraints such as cyclic phase order and minimum/maximum duration for each traffic phase. These issues make existing methods infeasible to implement for real-world applications. In this paper, we propose an RL-based multi-intersection traffic light control model with a simple yet effective combination of state, reward, and action definitions. The proposed model uses a novel pressure method called Biased Pressure (BP). We use a state-of-the-art advantage actor-critic learning mechanism in our model. Due to the decentralized nature of our state, reward, and action definitions, we achieve a scalable model. The performance of the proposed method is compared with related methods using both synthetic and real-world datasets. Experimental results show that our method outperforms the existing cyclic phase control methods with a significant margin in terms of throughput and average travel time. Moreover, we conduct ablation studies to justify the superiority of the BP method over the existing pressure methods.
Neurosteroids as stress modulators and neurotherapeutics: lessons from the retina

Neurosteroids are rapidly emerging as important new therapies in neuropsychiatry, with one such agent, brexanolone, already approved for treatment of postpartum depression, and others on the horizon. These steroids have unique properties, including neuroprotective effects that could benefit a wide range of brain illnesses including depression, anxiety, epilepsy, and neurodegeneration. Over the past 25 years, our group has developed ex vivo rodent models to examine factors contributing to several forms of neurodegeneration in the retina. In the course of this work, we have developed a model of acute closed angle glaucoma that involves incubation of ex vivo retinas under hyperbaric conditions and results in neuronal and axonal changes that mimic glaucoma. We have used this model to determine neuroprotective mechanisms that could have therapeutic implications. In particular, we have focused on the role of both endogenous and exogenous neurosteroids in modulating the effects of acute high pressure. Endogenous allopregnanolone, a major stress-activated neurosteroid in the brain and retina, helps to prevent severe pressure-induced retinal excitotoxicity but is unable to protect against degenerative changes in ganglion cells and their axons under hyperbaric conditions. However, exogenous allopregnanolone, at a pharmacological concentration, completely preserves retinal structure and does so by combined effects on gamma-aminobutyric acid type A receptors and stimulation of the cellular process of macroautophagy. Surprisingly, the enantiomer of allopregnanolone, which is inactive at gamma-aminobutyric acid type A receptors, is equally retinoprotective and acts primarily via autophagy. Both enantiomers are also equally effective in preserving retinal structure and function in an in vivo glaucoma model. These studies in the retina have important implications for the ongoing development of allopregnanolone and other neurosteroids as therapeutics for neuropsychiatric illnesses.