ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Cloud tomography applied to sky images: A virtual testbed

Two tomographic techniques are applied to two simulated sets of sky images with different cloud fraction. The Algebraic Reconstruction Technique (ART) is applied to optical depth maps from sky images to reconstruct 3-D cloud extinction coefficients without considering multiple scattering effects. Reconstruction accuracy is explored for different products, including surface irradiance and extinction coefficients, and as a function of the number of available sky imagers and setup distance. Increasing the number of imagers improves the accuracy of the 3-D reconstruction: for surface irradiance, the error decreases significantly up to four imagers at which point the improvements become marginal. But using nine imagers gives more robust results in practical situations in which the circumsolar region of images has to be excluded due to poor cloud detection. The ideal distance between imagers was also explored: for a cloud height of 1 km, increasing distance up to 3 km (the domain length) improved the 3-D reconstruction. An iterative reconstruction technique that iteratively updated the source function improved the results of the ART by minimizing the error between input red radiance images and reconstructed red radiance simulations. For the best case of a nine-imager deployment, the ART and iterative method resulted in 53.4% and 33.6% relative mean absolute error for the extinction coefficients, respectively.
Covalent Organic Frameworks as Efficient Photoinitiators and Cross-Linkers To Fabricate Highly Stretchable Hydrogels

In this work, two kinds of imine-type covalent organic framework (COF) nanoparticles are demonstrated as efficient photocatalytic initiators to trigger the free-radical polymerization of acrylamide (AM) to prepare polyacrylamide (PAM) hydrogels under visible light irradiation, without any assistance from the co-initiator. Simultaneously, the COF nanoparticles bearing vinyl side groups (COF-V) promote covalent cross-linking of the polymer chains, which significantly reinforces the mechanical properties of the nanocomposite hydrogel. The obtained PAM/COF-V hydrogel is highly stretchable with an extraordinary elongation up to 3300% strain. On the other hand, the COF nanoparticles modified with methoxy moieties (COF-OMe) endow the resulting PAM/COF-OMe hydrogel with a promising fluorescence feature. In addition, this strategy provides a visible-light-regulated photocatalytic polymerization approach with a simplified recipe to fabricate COF-based nanocomposite hydrogels or resins with diverse functions.
Loud Auditory Distractors Are More Difficult to Ignore After All

Working memory performance is markedly disrupted when task-irrelevant sound is played during item presentation or retention. In a preregistered replication study, we systematically examined the role of intensity in two types of auditory distraction. The first type of distraction is the changing-state effect (i.e., increased disruption by changing-state relative to steady-state sequences). The second type is the auditory deviant effect (i.e., increased disruption by auditory deviant relative to steady-state sequences). In previous experiments, the changing-state effect was independent of intensity. Whether a deviation in intensity leads to an increase in disruption has not yet been examined. We replicated the classic finding that the increased disruption by changing-state relative to steady-state sequences is independent of intensity. Contrary to previous studies, we found an unexpected main effect of intensity. Steady-state and changing-state sequences presented at 75 dB(A) were more disruptive than presented at 45 dB(A), suggesting that intensity plays a more important role than previously assumed in the disruption of working memory performance. Furthermore, we tested the prediction of the violation of expectancy account, according to which deviant distractors at a lower and higher intensity than the rest of the sequence should be equally disruptive. Our results were consistent with this prediction.
Profiling of key heat shock proteins and their relationship with male sexual behavior and seminal characteristics in Kankrej (Bos indicus) breeding bulls during different seasons

The goal of this study is to use indirect ELISA to determine the concentration of major heat shock proteins (Hsps) in Kankrej (Bos indicus) breeding bulls and their relationship with certain male phenotypic traits including sexual behavior, sperm quality, and bull fertility in different seasons. The seasonal fluctuation in the concentration of three major Hsps (60, 70, and 90) was determined using an indirect enzyme-linked immunosorbent assay (ELISA). According to the findings, Hsps levels are significantly higher during the summer season and are associated with both fresh and post-thawed semen quality traits in Kankrej breeding bulls. The better sexual behavior of bulls and seminal parameters of fresh or thawed semen was observed in the winter season together with the lower concentrations of HSPs. These could suggest negative association between HSPs with bull sexual behavior and seminal parameters. As a result, the concentration of Hsps in breeding bulls may be a useful indicator for determining fertility traits.
Dynamic Binary Countdown for Massive IoT Random Access in Dense 5G Networks

Massive connectivity for Internet of Things applications is expected to challenge the way access reservation protocols are designed in 5G networks. Since the number of devices and their density are envisioned to be orders of magnitude larger, state-of-the-art access reservation, random access (RA) procedure, might be a bottleneck for end-to-end delay. This would be especially challenging for burst arrival scenarios: semisynchronous triggering of a large number of devices due to a common event (blackout, emergency alarm, etc.). In this paper, to improve RA procedure scalability, we propose to combine binary countdown contention resolution (BCCR) with the state-of-theart access class barring (ACB). We present a joint analysis of ACB and BCCR and apply a framework for treating RA as a biobjective optimization, minimizing the resource consumption and maximizing the throughput of the procedure in every contention round. We use this framework to devise dynamic load-adaptive algorithm and simulatively illustrate that the proposed algorithm reduces the burst resolution delay while consuming less resources compared to the state-of-the-art techniques.
Slice Sorting for Unequal Loss Protection of Video Streams

In this letter, we propose a novel unequal loss protection scheme, which allocates FEC codes to video slices according to their impact on the GOP distortion. This is evaluated taking the concealment procedure and the drift effect into account. Simulation results show that the proposed algorithm outperforms state-of-the-art approaches, reducing the gap with the error-free performance curve. Moreover, the complexity of the additional stage required to pilot the protection allocation stage is negligible with respect to traditional ULP schemes.
Towards less mutilating treatments in patients with advanced non-melanoma skin cancers by earlier use of immune checkpoint inhibitors

Merkel cell carcinoma (MCC), advanced cutaneous squamous cell carcinoma (cSCC), and advanced basal cell carcinoma (BCC) are rare, and the often frail patients may require potentially mutilating local treatments. Immune checkpoint inhibitors (ICIs) are effective in melanoma and are moving towards the neoadjuvant setting. This systematic review explores data supporting the transition of ICIs from the metastatic to the (neo)adjuvant setting non-melanoma skin cancer (NMSC) and describes how knowledge from melanoma can be utilized. ICI response rates in advanced NMSC and melanoma are comparable. Five early phase studies show effectivity of neoadjuvant ICIs in melanoma and adjuvant treatment is standard-of-care. Eight adjuvant and 12 neoadjuvant ICI studies are ongoing for NMSC. Encouragingly, data from two small neoadjuvant ICI studies in NMSC, demonstrated complete responses in approximately half of patients. In conclusion, neoadjuvant ICI treatment has potential to avert mutilating treatments in NMSC. Progress can be accelerated by learning from melanoma.
Learning New Parts for Landmark Localization in Whole-Body CT Scans

The goal of this work is to reliably and accurately localize anatomical landmarks in 3-D computed tomography scans, particularly for the deformable registration of whole-body scans, which show huge variation in posture, and the spatial distribution of anatomical features. Parts-based graphical models (GM) have shown attractive properties for this task because they capture naturally anatomical relationships between landmarks. Unfortunately, standard GMs are learned from manually annotated training images and the quantity of landmarks is limited by the high cost of expert annotation. We propose a novel method that automatically learns new corresponding landmarks from a database of 3-D whole-body CT scans, using a limited initial set of expert-labeled ground-truth landmarks. The newly learned landmarks, called B-landmarks, are used to build enriched GMs. We compare our method of deformable registration based on such GM landmarks to a conventional deformable registration method and to a "baseline" state-of-the-art GM. The results show our method finds new relevant anatomical correspondences and improves by up to 35% the matching accuracy of highly variable skeletal and soft-tissue landmarks of clinical interest.
Gender Affirmation Surgery on the Rise: Analysis of Trends and Outcomes

Purpose: Gender-affirming surgery (GAS) has become an important component of the treatment of gender dysphoria. Although the frequency of these procedures is on the rise, a complete safety profile has yet to be established. The goal of our study is to analyze the trends and outcomes of these surgical procedures. Methods: All patients with a primary diagnosis of gender dysphoria undergoing GAS were identified from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database between the years 2009 and 2018. Patient demographics and 30-day postoperative outcomes were recorded. We performed a multivariate logistic regression for postoperative complications, controlling for several confounding variables. Results: We identified 2956 patients, of which 1767 (59.78%) were transgender men and 1189 (40.22%) were transgender women. The number of patients undergoing GAS per year increased from 7 in 2010 to 1069 in 2018, a 152-fold increase. For patients undergoing top surgery, Black race (odds ratio [OR] = 2.255, 95% confidence interval [CI] 1.189-4.277, p = 0.013) and diabetes (OR = 4.156, 95% CI 1.571-10.999, p = 0.004) were independent predictors of 30-day postoperative complications. For patients undergoing bottom surgery, total operative time in minutes (OR = 1.005, 95% CI 1.003-1.007, p = 0.001) was an independent predictor of 30-day postoperative complications. Conclusion: The demand for GAS has increased exponentially since 2014. While postoperative complication rates are acceptable, Black race was shown to be an independent predictor of postoperative morbidity in patients undergoing top surgery, a finding that calls for further investigation of racial disparities among transgender patients.
Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data

Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness. However, the widely existing distribution difference and sample mismatch between different data domains hinder the generalisation of semi-supervised learning. In this study, we alleviate these problems by proposing an Adaptive Hierarchical Dual Consistency (AHDC) for the semi-supervised LA segmentation on cross-domain data. The AHDC mainly consists of a Bidirectional Adversarial Inference module (BAI) and a Hierarchical Dual Consistency learning module (HDC). The BAI overcomes the difference of distributions and the sample mismatch between two different domains. It mainly learns two mapping networks adversarially to obtain two matched domains through mutual adaptation. The HDC investigates a hierarchical dual learning paradigm for cross-domain semi-supervised segmentation based on the obtained matched domains. It mainly builds two dual-modelling networks for mining the complementary information in both intra-domain and inter-domain. For the intra-domain learning, a consistency constraint is applied to the dual-modelling targets to exploit the complementary modelling information. For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains. We demonstrated the performance of our proposed AHDC on four 3D late gadolinium enhancement cardiac MR (LGE-CMR) datasets from different centres and a 3D CT dataset. Compared to other state-of-the-art methods, our proposed AHDC achieved higher segmentation accuracy, which indicated its capability in the cross-domain semi-supervised LA segmentation.
Analyzing wet weather flow management using state of the art tools

Optimal secondary clarifier performance is crucial to meet treatment requirements, especially when treating peak wet weather flows (PWWFs), to prevent high effluent suspended solids (ESS) concentrations and elevated sludge blankets. A state-of-the-art computational fluid dynamic (CFD) model was successfully used as a design and diagnostic tool to optimize performance for municipal wastewater treatment plants subject to significant PWWFs. Two case studies are presented. For Case Study 1, the model was used to determine the number of secondary clarifiers that will be necessary to treat future PWWF conditions for a plant under design. For Case Study 2, the model was used to identify modifications that are currently being made to increase the clarifier capacity for handling PWWF.
Bioactive Secondary Plant Metabolites from Euphorbia umbellata (PAX) BRUYNS (Euphorbiaceae)

The species Euphorbia umbellata has been used to treat inflammatory diseases, cancer, and ulcers. Biological activities reported in the literature, including antiproliferative, cytotoxic and anti-inflammatory, are attributed to the chemical constituents present in its composition as terpenes and polyphenolic compounds. The most recurrently verified metabolites in the Euphorbiaceae family plant species are terpenes, of which euphol is a major constituent with broadly reported cytotoxic, antinociceptive and anti-inflammatory effects; it frequently appears in various extracts obtained from the plant. Euphol has a documented inhibitory effect on neutrophil chemotaxis and can modulate the complement system. Since complement system activation is intimately intertwined with autoimmune and inflammatory diseases, tumor growth promotion and metastasis, plant metabolites from Euphorbia umbellata might influence the outcomes of inflammatory processes. We believe that this is the first review presenting the current knowledge on Euphorbia umbellata secondary metabolites and their biological activities.
Mechanochemical Preparation of Edge-Selectively justify Hydroxylated Graphene Nanosheets Using Persulfate via a Sulfate Radical-Mediated Process

The production of water-dispersed graphene with low defects remains a challenge. The dry ball milling of graphite with additives produces edge-selectively functionalized graphene. However, the "inert" additives require a long milling time and cause inevitable in-plane defects. Here, the mechanochemical reaction of graphite with persulfate solved the above drawback and produced edge-selectively hydroxylated graphene (EHG) nanosheets through a 2 h ball-milling and a subsequent 0.5 h sonication. The mechanochemical cleavage of persulfate yielded SO4 ⋅- to spontaneously oxidize graphite to form the carbon radical cations selectively at edges, followed by hydroxylation with water of moisture. Because the O-O bond dissociation energy of persulfate is 20 % of the graphitic C-C bond, the rather low milling energy allowed the hydroxylation of graphite at edges with nearly no in-plane defects. The obtained EHG showed high water-dispersibility and excellent photothermal and electrochemical properties, thereby opening up a new door to fabricate graphene-based composites.
The advances of calcium oxalate calculi associated drugs and targets

Kidney stones constitute a disease of the urinary system of high incidence that has only a few available stone dissolving drugs as treatment options. The mechanism of calcium oxalate stone formation is still largely unclear. Various aspects and theories for initiation and formation of the renal stones have been suggested, and the complex multistep formation process of the kidney stones includes supersaturation, nucleation, growth and aggregation of a crystal, and crystal retention in cells after adhesion. During the initial stage of crystal formation, high concentrations of oxalate exposure may damage the renal tubular cells and cause oxidative stress after which the cells may be attached to the crystal thus supporting the oxalate-induced injury as the driving factor of crystal precipitation and cellular adhesion. However, at present, although various drugs targeting kidney stones have been proposed and evaluated both in vitro and in vivo, clinical drugs for stone dissolution have rarely been explored. Moreover, numerous advances in renal calcium oxalate stone associated target and drugs warrant their summarization until now, which could be further discussed and may provide potential ideas or options for exploration of renal calcium oxalate stone treatment targets and drugs.
FLDNet: Light Dense CNN for Fingerprint Liveness Detection

Fingerprint liveness detection has gained increased attention recently due to the growing threat of spoof presentation attacks. Among the numerous attempts to deal with this problem, the Convolutional Neural Networks (CNN) based methods have shown impressive performance and great potential. However, there is a need for improving the generalization ability and reducing the complexity. Therefore, we propose a lightweight (0.48M parameters) and efficient network architecture, named FLDNet, with an attention pooling layer which overcomes the weakness of Global Average Pooling (GAP) in fingerprint anti-spoofing tasks. FLDNet consists of modified dense blocks which incorporate the residual path. The designed block architecture is compact and effectively boosts the detection accuracy. Experimental results on two datasets, LivDet 2013 and 2015, show the proposed approach achieves state-of-the-art performance in intra-sensor, cross-material and cross-sensor testing scenarios. For example, on LivDet 2015 dataset, FLDNet achieves 1.76 & x0025; Average Classification Error (ACE) over all sensors and 3.31 & x0025; against unkown spoof materials compared to 2.82 & x0025; and 5.45 & x0025; achieved by state-of-the-art methods.
Angular selective window systems: Assessment of technical potential for energy savings

Static angular selective shading systems block direct sunlight and admit daylight within a specific range of incident solar angles. The objective of this study is to quantify their potential to reduce energy use and peak demand in commercial buildings using state-of-the art whole-building computer simulation software that allows accurate modeling of the behavior of optically-complex fenestration systems such as angular selective systems. Three commercial systems were evaluated: a micro-perforated screen, a tubular shading structure, and an expanded metal mesh. This evaluation was performed through computer simulation for multiple climates (Chicago, Illinois and Houston, Texas), window-to-wall ratios (0.15-0.60), building codes (ASHRAE 90.1-2004 and 2010) and lighting control configurations (with and without). The modeling of the optical complexity of the systems took advantage of the development of state-of-the-art versions of the EnergyPlus, Radiance and Window simulation tools. Results show significant reductions in perimeter zone energy use; the best system reached 28% and 47% savings, respectively, without and with daylighting controls (ASHRAE 90.1-2004, south facade, Chicago, WWR= 0.45). Angular selectivity and thermal conductance of the angle-selective layer, as well as spectral selectivity of low-emissivity coatings, were identified as factors with significant impact on performance. (C) 2014 Elsevier B.V. All rights reserved.
Poisson image denoising by piecewise principal component analysis and its application in single-particle X-ray diffraction imaging

This study describes an improved method for Poisson image denoising that is based on a state-of-the-art Poisson denoising approach known as non-local principal component analysis (NLPCA). The new method is referred to as PieceWise Principal Component Analysis (PWPCA). In PWPCA, the given image is first split into pieces, then NLPCA is run on each image piece, and finally the entire image is reconstituted by a weighted combination of the NLPCA-processed image pieces. Using standard test images with Poisson noise, the authors show that PWPCA restores images more effectively than state-of-the-art Poisson denoising approaches. In addition, and to the best of their knowledge, they show the first application of such approaches to single-particle X-ray free-electron laser (XFEL) data. They show that the resolution of three-dimensional reconstruction from XFEL diffraction images is improved when the data are preprocessed with PWPCA. XFELs are currently under rapid development to allow high-resolution biomolecular structure determination at near-physiological conditions. Data analysis methods developments follow these technological advances and are expected to have high impact in structural biology and drug design. This study contributes to these developments. As little experimental single-particle XFEL data is available still, the XFEL experiments shown here were performed with simulated data.
Two decades of rice research in Indonesia and the Philippines: A systematic review and research agenda for the social sciences

While rice studies are abundant, they usually focus on macro-level rice production and yield data, genetic diversity, cultivar varieties, and agrotechnological innovations. Moreover, many of these studies are either region-wide or concentrated on countries in the Global North. Collecting, synthesizing, and analyzing the different themes and topic areas in rice research since the beginning of the 21st century, especially in the Global South, remain unaddressed areas. This study contributes to filling these research lacunae by systematically reviewing 2243 rice-related articles cumulatively written by more than 6000 authors and published in over 900 scientific journals. Using the PRISMA 2020 guidelines, this study screened and retrieved articles published from 2001 to 2021 on the various topics and questions surrounding rice research in Indonesia and the Philippines-two rice-producing and -consuming, as well as emerging economies in Southeast Asia. Using a combination of bibliometrics and quantitative content analysis, this paper discusses the productive, relevant, and influential rice scholars; key institutions, including affiliations, countries, and funders; important articles and journals; and knowledge hotspots in these two countries. It also discusses the contributions of the social sciences, highlights key gaps, and provides a research agenda across six interdisciplinary areas for future studies. This paper mainly argues that an interdisciplinary and comparative inquiry of potentially novel topic areas and research questions could deepen and widen scholarly interests beyond conventional natural science-informed rice research in Indonesia and the Philippines. Finally, this paper serves other researchers in their review of other crops in broader global agriculture.