ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Linking hydrological, hydraulic and water quality models for river water environmental capacity assessment

The water environment of a river network can self-clean to a certain extent; however, when the wastewater discharge load exceeds a certain threshold, the balance of nature is disrupted, leading to water pollution. This emphasises the urgent need to evaluate river water environmental capacity (RWEC) as a necessary parameter for sustainable development. However, to quantify the RWEC, it is important to estimate the hydrological and hydrodynamic factors in the basin, leading to the linking of these models. The present investigation aims to propose an integrated framework, named RWEC, consisting of hydrological and hydrodynamic models, a database system, and GIS to evaluate the water environmental carrying capacity of the selected river network. The rain - runoff (RR), hydrodynamic (HD), ecological (ECO), and RWEC models were used. Groups of data, including meteorology, hydrology, and the environment, combined with topographic data and waste sources, were applied. Groups of models and data were integrated into a seven-step framework to calculate the RWEC. The case study is a basin in Binh Duong Province, Vietnam and four pollutants were selected: NH4+, BOD5, NO3-, PO43-. The flow and water quality factors in the river basin in the study area were measured based on hydraulic models, and the water quality was calibrated. The role of hydrological, hydraulic, and water quality models in the RWEC calculation was clarified. According to the baseline and forecast scenarios, the calculation of the RWEC for the scenarios was performed. In the baseline scenario, RWECNH4+ is in the range (-283, -22) kg/day, RWECBOD5 ranges from (143, 3126) kg/day, RWECNO3- is in the range (-778, 2166) kg/day, and RWECPO43- is in the range (-31, 46) kg/day. The dependence of RWEC on environmental factors, self-cleaning factors, and the difference between the baseline and forecast scenarios were clarified.
MPC-Based Delay-Aware Fountain Codes for Real-Time Video Communication

With the prevalence of smart mobile devices and surveillance cameras, the traffic load within the Internet of Things (IoT) has shifted away from nonmultimedia data to multimedia traffics, particularly, the video content. However, the explosive demand for real-time video communication over wireless networks in IoT is constantly challenging both video coding and communication research communities. The state-of-the-art answer to this challenge is sliding-window-based delay-aware fountain (DAF) codes, which combine the channel-adaptive feature in rateless coding and the delay-aware feature in video coding. However, the high computational cost and large delay make it impractical for real-time streaming. To address this issue, we integrate the model predictive control (MPC) technique into DAF codes, so the complexity is lowered to an affordable level so that real-time video encoding is supported. Two schemes are developed in this paper: 1) DAF-S, the smallhorizon DAF codes and 2) DAF-O, the MPC-based DAF using video bit rate prediction. The advantages of both designs are validated through theoretical analysis and comprehensive experiments. The results of simulation experiments show that the decoding ratio of DAF-S is close to the global optimum in DAF codes, and higher than the other existing schemes; DAF-O outperforms the state-of-the-art real-time video communication algorithms.
Characterization of the electrical performance of different signal via geometries

Several signal via geometries are analyzed for across and through transmission by varying the drill-hole diameter, ratio of the pad and antipad diameters to drill-hole diameter, and number of ground planes. Time-and frequency-domain results showing impedance-, insertion-, and return-loss trends, which are useful for designing via geometries for high-speed transmission, art obtained. (C) 2005 Wiley Periodicals. Inc.
Global profiling of arginine dimethylation in regulating protein phase separation by a steric effect-based chemical-enrichment method

Protein arginine methylation plays an important role in regulating protein functions in different cellular processes, and its dysregulation may lead to a variety of human diseases. Recently, arginine methylation was found to be involved in modulating protein liquid-liquid phase separation (LLPS), which drives the formation of different membraneless organelles (MLOs). Here, we developed a steric effect-based chemical-enrichment method (SECEM) coupled with liquid chromatography-tandem mass spectrometry to analyze arginine dimethylation (DMA) at the proteome level. We revealed by SECEM that, in mammalian cells, the DMA sites occurring in the RG/RGG motifs are preferentially enriched within the proteins identified in different MLOs, especially stress granules (SGs). Notably, global decrease of protein arginine methylation severely impairs the dynamic assembly and disassembly of SGs. By further profiling the dynamic change of DMA upon SG formation by SECEM, we identified that the most dramatic change of DMA occurs at multiple sites of RG/RGG-rich regions from several key SG-contained proteins, including G3BP1, FUS, hnRNPA1, and KHDRBS1. Moreover, both in vitro arginine methylation and mutation of the identified DMA sites significantly impair LLPS capability of the four different RG/RGG-rich regions. Overall, we provide a global profiling of the dynamic changes of protein DMA in the mammalian cells under different stress conditions by SECEM and reveal the important role of DMA in regulating protein LLPS and SG dynamics.
Age-friendly care in the Veterans Health Administration: Past, present, and future

The Veterans Health Administration (VHA) has long recognized the need for age-friendly care. VHA leadership anticipated the impact of aging World War II veterans on VA healthcare systems and in 1975 developed Geriatric Research, Education, and Clinical Centers (GRECCs) to meet this need. GRECCs catalyzed a series of innovations in geriatric models of care that span the continuum of care, most of which endure. These innovative care models also contributed to the evidence base supporting the present-day Age-Friendly Health Systems movement, with which VHA is inherently aligned. As both a provider of and payor for care, VHA is strongly incentivized to promote coordination across the continuum of care, with resultant cost savings. VHA is also a major contributor to developing the workforce that is essential for the provision of age-friendly care. As VHA continues to develop and refine innovative geriatric models of care, policymakers and non-VHA health care systems should look to VHA programs as exemplars for the development and implementation of age-friendly care.
A broad-spectrum integrative design for cancer prevention and therapy: The challenge ahead

Despite exciting advances in targeted therapies, high drug costs, marginal therapeutic benefits and notable toxicities are concerning aspects of today's cancer treatments. This special issue of Seminars in Cancer Biology proposes a broad-spectrum, integrative therapeutic model to complement targeted therapies. Based on extensive reviews of the cancer hallmarks, this model selects multiple high-priority targets for each hallmark, to be approached with combinations of low-toxicity, low-cost therapeutics, including phytochemicals, adapted to the well-known complexity and heterogeneity of malignancy. A global consortium of researchers has been assembled to advance this concept, which is especially relevant in an era of rapidly expanding capacity for genomic tumor analyses, alongside alarming growth in cancer morbidity and mortality in low- and middle-income nations.
Denormalization of visibilities for in-orbit calibration of interferometric radiometers

This paper reviews the relative calibration of an interferometric radiometer taking into account the experimental results of the first batch of receivers developed in the frame of the European Space Agency's Soil Moisture and Ocean Salinity mission. Measurements show state-of-the-art baseline performance as long as the system is capable of correcting the effect of orbital, temperature swing. A method to validate internal calibration during in-orbit deep-sky views and to correct linearity errors is also presented.
A DCT Approximation for Image Compression

An orthogonal approximation for the 8-point discrete cosine transform (DCT) is introduced. The proposed transformation matrix contains only zeros and ones; multiplications and bit-shift operations are absent. Close spectral behavior relative to the DCT was adopted as design criterion. The proposed algorithm is superior to the signed discrete cosine transform. It could also outperform state-of-the-art algorithms in low and high image compression scenarios, exhibiting at the same time a comparable computational complexity.
Putting the Radio in "Software-Defined Radio": Hardware Developments for Adaptable RF Systems

The prospects for and the state of the art of adaptable RF hardware are reviewed, focusing primarily on the traditional frequency planning bottleneck, the filtering stages. First, a case is made that even banded systems can be greatly impacted by a modest amount of tuning. This is done by showing the results of a traditional fixed system in an unlicensed band upgraded with a programmable front-end filter. Next, a system built specifically for wideband tuning is shown that enables band selection across the 20-MHz-6-GHz-band. Cooperative operation of multiple colocated nodes is enabled by high-quality pre-LNA filtering across the bands of operation. Future capabilities of adaptable systems are shown by reviewing the state of the art of adaptable systems, heading toward a field-programmable filter array in which a sea of resonators are dynamically interconnected to create a transfer function on demand. Additionally, a novel synthesis approach is highlighted in which multiple filters can cooperate gracefully without crossover issues between the bands. This approach allows for a vast number of filter states by turning on and off pass-bands without affecting the adjacent bands. The advancements in adaptable hardware will enable new classes of RF systems which much more efficiently utilize the spectrum.
License Plate Detection via Information Maximization

License plate (LP) detection in the wild remains challenging due to the diversity of environmental conditions. Nevertheless, prior solutions have focused on controlled environments, such as when LP images frequently emerge as from an approximately frontal viewpoint and without scene text which might be mistaken for an LP. However, even for state-of-the-art object detectors, their detection performance is not satisfactory for real-world environments, suffering from various types of degradation. To solve these problems, we propose a novel end-to-end framework for robust LP detection, designed for such challenging settings. Our contribution is threefold: (1) A novel information-theoretic learning that takes advantage of a shared encoder, an LP detector and a scene text detector (excluding LP) simultaneously; (2) Localization refinement for generalizing the bounding box regression network to complement ambiguous detection results; (3) a large-scale, comprehensive dataset, LPST-110K, representing real-world unconstrained scenes including scene text annotations. Computational tests show that the proposed model outperforms other state-of-the-art methods on a variety of challenging datasets.
Learning From Multiple Datasets With Heterogeneous and Partial Labels for Universal Lesion Detection in CT

Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations). When training a lesion detector on a partially-labeled dataset, the missing annotations will generate incorrect negative signals and degrade the performance. Besides DeepLesion, there are several small single-type datasets, such as LUNA for lung nodules and LiTS for liver tumors. These datasets have heterogeneous label scopes, i.e., different lesion types are labeled in different datasets with other types ignored. In this work, we aim to develop a universal lesion detection algorithm to detect a variety of lesions. The problem of heterogeneous and partial labels is tackled. First, we build a simple yet effective lesion detection framework named Lesion ENSemble (LENS). LENS can efficiently learn from multiple heterogeneous lesion datasets in a multi-task fashion and leverage their synergy by proposal fusion. Next, we propose strategies to mine missing annotations from partially-labeled datasets by exploiting clinical prior knowledge and cross-dataset knowledge transfer. Finally, we train our framework on four public lesion datasets and evaluate it on 800 manually-labeled sub-volumes in DeepLesion. Our method brings a relative improvement of 49% compared to the current state-of-the-art approach in the metric of average sensitivity. We have publicly released our manual 3D annotations of DeepLesion online.(1) (1) https://github.com/viggin/DeepLesion_manual_test_set
ARTS, an AR Tourism System, for the Integration of 3D Scanning and Smartphone AR in Cultural Heritage Tourism and Pedagogy

Interactions between cultural heritage, tourism, and pedagogy deserve investigation in an as-built environment under a macro- or micro-perspective of urban fabric. The heritage site of Shih Yih Hall, Lukang, was explored. An Augmented Reality Tourism System (ARTS) was developed on a smartphone-based platform for a novel application scenario using 3D scans converted from a point cloud to a portable interaction size. ARTS comprises a real-time environment viewing module, a space-switching module, and an Augmented Reality (AR) guide graphic module. The system facilitates scenario initiations, projection and superimposition, annotation, and interface customization, with software tools developed using ARKit((R)) on the iPhone XS Max((R)). The three-way interaction between urban fabric, cultural heritage tourism, and pedagogy was made possible through background block-outs and an additive or selective display. The illustration of the full-scale experience of the smartphone app was made feasible for co-relating the cultural dependence of urban fabric on tourism. The great fidelity of 3D scans and AR scenes act as a pedagogical aid for students or tourists. A Post-Study System Usability Questionnaire (PSSUQ) evaluation verified the usefulness of ARTS.
Research Note: Intergenerational Transmission Is Not Sufficient for Positive Long-Term Population Growth

All leading long-term global population projections agree on continuing fertility decline, resulting in a rate of population size growth that will continue to decline toward zero and would eventually turn negative. However, scholarly and popular arguments have suggested that because fertility transmits intergenerationally (i.e., higher fertility parents tend to have higher fertility children) and is heterogeneous within a population, long-term population growth must eventually be positive, as high-fertility groups come to dominate the population. In this research note, we show that intergenerational transmission of fertility is not sufficient for positive long-term population growth, for empirical and theoretical reasons. First, because transmission is imperfect, the combination of transmission rates and fertility rates may be quantitatively insufficient for long-term population growth: higher fertility parents may nevertheless produce too few children who retain higher fertility preferences. Second, today even higher fertility subpopulations show declining fertility rates, which may eventually fall below replacement (and in some populations already are). Therefore, although different models of fertility transmission across generations reach different conclusions, depopulation is likely under any model if, in the future, even higher fertility subpopulations prefer and achieve below-replacement fertility. These results highlight the plausibility of long-term global depopulation and the importance of understanding the possible consequences of depopulation.
Complex community health and social care interventions - Which features lead to reductions in hospitalizations for ambulatory care sensitive conditions? A systematic literature review

Preventing hospitalizations due to ambulatory care sensitive conditions (ACSCs) is traditionally the responsibility of primary care. The determinants of ACSC hospitalizations, however, are not purely medical, but also influenced by other factors like patients' social and personal circumstances. Interventions that include or consist entirely of community health services and social care could potentially reduce the ACSC hospitalization rate. Comparisons of the features of successful interventions of this nature, however, are still lacking. We therefore conducted a systematic review of the literature to identify out-of-hospital interventions that (a) included aspects or consisted entirely of community health services and social care and (b) analyzed the ACSC hospitalization rate as an outcome measure. We identified papers reporting the results of 32 interventions and extracted structural and behavioral features to determine which of these were shared by most or all of the successful interventions. We found that all of the successful interventions included a primary care physician and provided care management. Moreover, most of the successful interventions were characterized by a high degree of interconnectedness between professional groups and provided care within so-called health care homes. We also identified a set of care coordination activities that were implemented in most of the successful interventions. Policy makers may wish to consider adopting these features when designing interventions that aim to reduce the ACSC hospitalization rate.
Reflect on emotional events from an observer's perspective: a meta-analysis of experimental studies

Self-distancing has been proposed as an emotion regulation strategy to reduce the duration and intensity of emotions. This meta-analysis synthesised 48 studies and 102 effect sizes examining the effects of self-distancing on emotion regulation. The results showed an overall significant, small effect of self-distancing in attenuating emotional responses (Hedges' g = -0.26, 95%CI: [-0.36, -0.15]). Moderator analyses highlighted the efficacy of one intervention feature: approach. Stronger effect was associated with the visual and verbal approach to process emotional events, in comparison to the visual only approach and the pronouns approach. The effectiveness of self-distancing was consistent across other intervention features (context, stimuli, time, emotional outcome) and individual characteristics (emotional vulnerability, age, culture). These findings suggest that self-distancing is effective in emotion regulation when people externalise and articulate thoughts through writing and talking. Practical implications were discussed in relation to the design of interventions to enhance emotional well-being.
Aortopulmonary window, aortic arch interruption, and anomalous origin of the right pulmonary artery in a neonate with Goldenhar syndrome

The combination of aortopulmonary window, interruption of the aortic arch, and anomalous origin of the right pulmonary artery from the ascending aorta is a rare and complex congenital cardiac malformation. Despite good prenatal care in our case, this cardiac anomaly was not detected prior to birth. Untreated infants who do not undergo surgical correction have a mortality rate of 70% in their first year, and 30% will die within the first 3 months of life.
A feature extraction based support vector machine model for rectal cancer T-stage prediction using MRI images

Accurate clinical cancer T-stage diagnosis is crucial for effective treatment. However, it is difficult, time-consuming, and laborious for physicians to recognize T-stage manually using rectal Magnetic Resonance Imaging (MRI) images. Machine learning methods have played important roles in medical image processing. With the goal of automatic rectal cancer T-stage prediction, we train the proposed Feature Extraction based Support Vector Machine (FE-SVM) model with the newly acquired dataset consisting of 147 patients' MRI images with primary rectal cancer. Our method adapts SVM as the training framework as SVM is effective enough for dealing with small datasets as opposed to state-of-the-art deep learning models. FE-SVM firstly extracts image similarity as an initial feature because the feature of image similarity can better reflect the differences among various types of MRI images, and the final 10-dimensional features are obtained by a 5-layers Autoencoder. To evaluate the performance of FE-SVM, we compared it with six benchmark models: CNN, Alexnet, Resnet18, Resnet50, Capsule Network, and Random Forest. FE-SVM outperforms these state-of-the-art models with significant evaluation scores.
A broadly conserved fungal alcohol oxidase (AOX) facilitates fungal invasion of plants

Alcohol oxidases (AOXs) are ecologically important enzymes that facilitate a number of plant-fungal interactions. Within Ascomycota they are primarily associated with methylotrophy, as a peroxisomal AOX catalysing the conversion of methanol to formaldehyde in methylotrophic yeast. In this study we demonstrate that AOX orthologues are phylogenetically conserved proteins that are common in the genomes of nonmethylotrophic, plant-associating fungi. Additionally, AOX orthologues are highly expressed during infection in a range of diverse pathosystems. To study the role of AOX in plant colonization, AOX knockout mutants were generated in the broad host range pathogen Sclerotinia sclerotiorum. Disease assays in soybean showed that these mutants had a significant virulence defect as evidenced by markedly reduced stem lesions and mortality rates. Chemical genomics suggested that SsAOX may function as an aromatic AOX, and growth assays demonstrated that ΔSsAOX is incapable of properly utilizing plant extract as a nutrient source. Profiling of known aromatic alcohols pointed towards the monolignol coniferyl alcohol (CA) as a possible substrate for SsAOX. As CA and other monolignols are ubiquitous among land plants, the presence of highly conserved AOX orthologues throughout Ascomycota implies that this is a broadly conserved protein used by ascomycete fungi during plant colonization.
SMT Solder Joint Inspection via a Novel Cascaded Convolutional Neural Network

Due to the excellent self-learning ability of deep learning, we propose a novel deep-learning-based method to inspect surface-mount technology (SMT) solder joints in this paper. In contrast to the state-of-the-art learning-based methods in which low-level features are extracted before learning, our method directly implements the inspection task without low-level feature extraction, which is based on a novel cascaded convolutional neural network (CNN). Three kinds of CNNs with different network parameters compose the proposed cascaded CNN. First, one kind of CNN is employed to adaptively learn the regions of interest (ROIs) of SMT solder joint images. Then, both the learned ROIs and the entire solder joint images are fed into the other two kinds of CNNs, respectively. Finally, inspection results are achieved by the learned cascaded CNN. Comparison experiments indicate that our proposed method can achieve more excellent inspection performance for SMT solder joints than that of the state-of-the-art methods.