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To address privacy and ethical issues in using health data for machine learning, we evaluate the scalability of advanced synthetic data generation methods like GANs, VAEs, copulaGAN, and transformer models specifically for patient service utilization data. Our study examines five models on data from a Canadian health authority, focusing on training and generation efficiency, data resemblance, and practical utility. Our findings indicate that statistical models excel in efficiency, while most models produce synthetic data that closely mirrors real data, and is also useful for real-world applications.

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We demonstrate the synthesis of biogenic supported silver spiked star architectures and their application to increase the electromagnetic field intensity at its tips that enhance plasmon-coupled emission. Tecoma stans floral extract has been used to synthesize silver nanocubes and spiked stars. We observe ∼445-fold and ∼680-fold enhancements in spacer and cavity configurations, respectively, in the SPCE platform. The hotspot intensity and Purcell factor are evaluated by carrying out finite-difference time-domain (FDTD) simulations. Time-based studies are presented to modulate the sharpness of the edges wherein an increase in the tip sharpness with the increase in reaction time up to 5 h is observed. The unique morphology of the silver architectures allowed us to utilize them in biosensing application. A SPCE-based fluoroimmunoassay was performed, achieving a 1.9 pg/mL limit of detection of TNF-α cytokine. This combination of anisotropic architectures, SPCE and immunoassay prove to be a powerful platform for the ultrasensitive detection of biomarkers in surface-bound assays.

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Truss decomposition is a popular notion of hierarchical dense substructures in graphs. In a nutshell, k-truss is the largest subgraph in which every edge is contained in at least k triangles. Truss decomposition aims to compute k-trusses for each possible value of k. There are many works that study truss decomposition in deterministic graphs. However, in probabilistic graphs, truss decomposition is significantly more challenging and has received much less attention; state-of-the-art approaches do not scale well to large probabilistic graphs. Finding the tail probabilities of the number of triangles that contain each edge is a critical challenge of those approaches. This is achieved using dynamic programming which has quadratic run-time and thus not scalable to real large networks which, quite commonly, can have edges contained in many triangles (in the millions). To address this challenge, we employ a special version of the Central Limit Theorem (CLT) to obtain the tail probabilities efficiently. Based on our CLT approach we propose a peeling algorithm for truss decomposition that scales to large probabilistic graphs and offers significant improvement over state-of-the-art. We also design a second method which progressively tightens the estimate of the truss value of each edge and is based on h-index computation. In contrast to our CLT-based approach, our h-index algorithm (1) is progressive by allowing the user to see near-results along the way, (2) does not sacrifice the exactness of final result, and (3) achieves all these while processing only one edge and its immediate neighbors at a time, thus resulting in smaller memory footprint. We perform extensive experiments to show the scalability of both of our proposed algorithms.

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Authentication plays a critical role in the security of quantum key distribution (QKD) protocols. We propose using Polynomial Hash and its variants for authentication of variable length messages in QKD protocols. Since universal hashing is used not only for authentication in QKD but also in other steps in QKD like error correction and privacy amplification, and also in several other areas of quantum cryptography, Polynomial Hash and its variants as the most efficient universal hash function families can be used in these important steps and areas, as well. We introduce and analyze several efficient variants of Polynomial Hash and, using deep results from number theory, prove that each variant gives an ε-almost-Δ-universal family of hash functions. We also give a general method for transforming any such family to an ε-almost-strongly universal family of hash functions. The latter families can then, among other applications, be used in the Wegman-Carter MAC construction which has been shown to provide a universally composable authentication method in QKD protocols. As Polynomial Hash has found many applications, our constructions and results are potentially of interest in various areas.

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

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BACKGROUND: Edinburgh Postnatal Depression Scale (EPDS) is a validated screening tool widely used to assess perinatal depression (PND). However, due to stigma associated with PND, respondents could answer sensitive questions differently depending on the mode of administration, especially in culturally and linguistically diverse country like India. The present study explored longitudinal differences in EPDS scores between self-administered and interviewer-administered modes. METHODS: 177 women from rural South India were administered EPDS, self-administration followed by interviewer-administered for four visits, twice each during prenatal and postnatal visits. EPDS scores were compared between the two modes descriptively, graphically and by repeated mixed measure models. Classification of antenatal depression (AD), postnatal depression (PD) and PND based on the two modes were compared by McNemar Chi-square test. Clinical and psychosocial characteristics were examined to identify factors associated with differences in the scoring modes. Concordance rates and Goodman Kruskal's Gamma coefficients were measured for individual EPDS items. RESULTS: Longitudinal EPDS scores and rates of AD, PD and PND were significantly higher in self-administered mode. Recent adverse life events were the only factor observed to be significantly associated with the differences between the two modes. Rank correlation and concordance rates suggested stronger association for EPDS items relating to anhedonia subscale and moderate/weaker association for EPDS items relating to anxiety/depression subscales. CONCLUSION: Our study findings suggest that the effect of mode of administration should be taken into account while using PND screening tools such as EPDS, especially in countries such as India with higher levels of illiteracy.

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The outbreak of the COVID-19 pandemic has had a major impact on the health and well-being of people with its long-term effect on lung function and oxygen uptake. In this work, we present a unique approach to augment the phosphorescence signal from phosphorescent gold(III) complexes based on a surface plasmon-coupled emission platform and use it for designing a ratiometric sensor with high sensitivity and ultrafast response time for monitoring oxygen uptake in SARS-CoV-2-recovered patients. Two monocyclometalated Au(III) complexes, one having exclusively phosphorescence emission (λPL = 578 nm) and the other having dual emission, fluorescence (λPL = 417 nm) and phosphorescence (λPL = 579 nm), were studied using the surface plasmon-coupled dual emission (SPCDE) platform for the first time, which showed 27-fold and 17-fold enhancements, respectively. The latter complex having the dual emission was then used for the fabrication of a ratiometric sensor for studying the oxygen quenching of phosphorescence emission with the fluorescence emission acting as an internal standard. Low-cost poly (methyl methacrylate) (PMMA) and biodegradable wood were used to fabricate the microfluidic chips for oxygen monitoring. The sensor showed a high sensitivity with a limit of detection ∼ 0.1%. Furthermore, real-time oxygen sensing was carried out and the response time of the sensor was calculated to be ∼0.2 s. The sensor chip was used for monitoring the oxygen uptake in SARS-CoV-2-recovered study participants, to assess their lung function post the viral infection.

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The quest for auxiliary plasmonic materials with lossless properties began in the past decade. In the current study, a unique plasmonic response is demonstrated from a stratified high refractive index (HRI)-graphene oxide (GO) and low refractive index (LRI)-polymethyl methacrylate (PMMA) multistack. Graphene oxide plasmon-coupled emission (GraPE) reveals the existence of strong surface states on the terminating layer of the photonic crystal (PC) framework. The chemical defects in GO thin film are conducive for unraveling plasmon hybridization within and across the multistack. We have achieved a unique assortment of metal-dielectric-metal (MDM) ensuing a zero-normal steering emission on account of solitons as well as directional GraPE. This has been theoretically established and experimentally demonstrated with a metal-free design. The angle-dependent reflectivity plots, electric field energy (EFI) profiles, and finite-difference time-domain (FDTD) analysis from the simulations strongly support plasmonic modes with giant Purcell factors (PFs). The architecture presented prospects for the replacement of metal-dependent MDM and surface plasmon-coupled emission (SPCE) technology with low cost, easy to fabricate, tunable soliton [graphene oxide plasmon-coupled soliton emission (GraSE)], and plasmon [GraPE] engineering for diverse biosensing applications. The superiority of the GraPE platform for achieving 1.95 pg mL-1 limit of detection of human IFN-γ is validated experimentally. A variety of nanoparticles encompassing metals, intermetallics, rare-earth, and low-dimensional carbon-plasmonic hybrids were used to comprehend PF and cavity hot-spot contribution resulting in 900-fold fluorescence emission enhancements on a lossless substrate, thereby opening the door to unique light-matter interactions for next-gen plasmonic and biomedical technologies.

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Enhancement of fluorescence emission from single-photon quantum emitters on plasmonic nanomaterials using surface plasmon-coupled emission (SPCE) platforms has seen significant advancements. In parallel, there has also been an exponential rise in applications involving two-dimensional (2D) transition-metal dichalcogenides (TMDs) that exhibit unique exciton-plasmon interactions. Although both these Frontier research areas have impacted the development of sensor and sensing technologies, no study coalesces these two arenas for translational applications. In this work, we use thin WS2 nanosheets for realizing 1000-fold fluorescence enhancement on the SPCE platform. Structure-dependent fluorescence enhancement exhibited by WS2 provides new insight into the use of TMDs and exciton-plasmon coupling in SPCE substrates. Cellphone-based detection of the emitting dipole is another unique aspect of this work that presents a low-cost alternative in comparison with high-end detectors.

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Globally, public health expenditure (PHE) is closely associated with Reproductive, Maternal, Newborn, Child Health, and Nutrition (RMNCHN) and Family Planning (FP) outcomes. In India, the role of PHE in shaping the progress towards the attainment of RMNCHN and FP-related Sustainable Development Goals (SDGs) is not widely documented. Using the four consecutive rounds of National Family Health Survey (NFHS), we have investigated the progress in RMNCHN and FP indicators and their association with PHE by applying robust econometric modelling. The findings suggest that although there is noticeable progress in the RMNCHN indicators from 1992-93-2015-16, India has failed to achieve RMNCHN targets related to Millennium Development Goals (MDGs). Lack of noteworthy correlation between FP indicators and PHE supports the argument that post National Rural Health Mission (2005), the core family welfare expenditure suffered a setback despite the absolute rise in PHE. However, correlation plots and the multivariate panel data regression analyses affirm that even with a moderate rise, PHE emerges as an important predictor of RMNCHN outcomes in the country. Thus, the road to achieving RMNCHN and FP-related SDGs demands to avoid austerity on PHE and strengthen the integration of RMNCHN and FP programmes at the operational level.

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As nonbiodegradable plastics continue to pollute our land and oceans, countries are starting to ban the use of single-use plastics. In this paper, we demonstrated the fabrication of wood-based microfluidic devices and their adaptability for single-use, point-of-care (POC) applications. These devices are made from easily sourced renewable materials for fabrication while exhibiting all the advantages of plastic devices without the problem of nonbiodegradable waste and cost. To build these wood devices, we utilized laser engraving and traditional mechanical methods and have adapted specific surface coatings to counter the wicking effect of wood. To demonstrate their versatility, wood microfluidic devices were adapted for (i) surface plasmon coupled enhancement (SPCE) of fluorescence for detection of proteins, (ii) T-/Y-geometry microfluidic channel mixers, and (iii) devices for rapid detection of microbial contamination. These provide proof of concept for the use of wooden platforms for POC applications. In this study, we measured the fluorescence intensities of recombinant green fluorescent protein (GFP) standards (ranging from 1.5-25 ng/µL) and 6XHis-G-CSF (ranging from 0.1-100 ng/µL) expressed in cell-free translation systems. All tested devices perform as well as or better than their plastic counterparts.

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We demonstrate for the first time the tuning of qubit emission based on cavity engineering on plasmonic silver thin films. This tunable transition from weak to strong coupling regime in plasmon-coupled fluorescence platform was achieved with the use of palladium nanocomposites. In addition to our recently established correlation between Purcell factor and surface plasmon-coupled emission enhancements, we now show that the qubit-cavity environment experiences the Purcell effect, Casimir force, internal fano resonance, and Rabi splitting. Finite-difference time-domain simulations and time correlated single photon counting studies helped probe the molecular structure of the radiating dipole, rhodamine-6G, in palladium-based nanocavities. The sensitivity of the qubit-cavity mode helped attain a DNA detection limit of 1 aM (attomolar) and multianalyte sensing at picomolar concentration with the use of a smartphone camera and CIE color space. We believe that this low-cost technology will lay the groundwork for mobile phone-based next-gen plasmonic sensing devices.

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We report cellphone-based detection of dopamine with attomolar sensitivity in clinical samples with the use of a surface plasmon-coupled emission (SPCE) platform. To this end, silver-coated carbon nanotubes were used as spacer and cavity materials on SPCE substrates to obtain up to 100-fold fluorescence enhancements. The presence of silver on the carbon nanotubes helped to overcome fluorescence quenching arising due to π-π interactions between the carbon nanotube and rhodamine 6G. The competing adsorption of dopamine versus rhodamine 6G on graphene oxide was utilized to develop this sensing platform.

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We demonstrate for the first time the use of Fe-based nanoparticles on N-doped graphene as spacer and cavity materials and study their plasmonic effect on the spontaneous emission of a radiating dipole. Fe-C-MF was produced by pyrolizing FeOOH and melamine formaldehyde precursor on graphene, while Fe-C-PH was produced by pyrolizing the Fe-phenanthroline complex on graphene. The use of the Fe-C-MF composite consisting of Fe-rich crystalline phases supported on N-doped graphene presented a spacer material with 116-fold fluorescence enhancements. On the other hand, the Fe-C-PH/Ag based cavity resulted in an 82-fold enhancement in Surface Plasmon-Coupled Emission (SPCE), with high directionality and polarization of Rhodamine 6G (Rh6G) emission owing to Casimir and Purcell effects. The use of a mobile phone as a cost-effective fluorescence detection device in the present work opens up a flexible perspective for the study of different nanomaterials as tunable substrates in cavity mode and spacer applications.

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