ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Health Outcomes of Kansas City's Vulnerable Patients Following Shutdown: An Assessment of Blood Pressure Among Sojourner Health Clinic Patients

Background In downtown Kansas City, patients who face homelessness or unstable housing situations may have been negatively affected by the shutdown of Sojourner Health Clinic (SOJO), a free student-run clinic that provides primary care predominantly to these patients. Research shows that blood pressure (BP) increases within weeks or months of interruption of antihypertensive therapy, especially in patients with advanced age and polypharmacy. Therefore, this study will examine how patients' blood pressure changed after the closure of Sojourner Health Clinic. Methods The study population consists of Sojourner Health Clinic patients who were seen both before March 2020 (shutdown) and during/after July 2020 (clinic reopening). Participants are selected at random. No additional data is collected outside of routine treatment for this institutional review board (IRB)-exempt project. A study coordinator reviews charts via Sojourner electronic medical record (EMR) and collects the latest BP available before March 2020 and the first BP available during/after July 2020. No identifying information is collected. The mean systolic pressures, mean diastolic pressures, and mean arterial pressures (MAP) are compared via paired t-test for statistical significance. Results There was a statistically significant decrease in patients' MAP and diastolic BP after the closure of the clinic. However, there was not a statistically significant change found in patients' systolic BP. The clinical significance of these results is limited by the minimal magnitude of change. Conclusions These findings run counter to our expectations since we believed that the closure of Sojourner Health Clinic would correlate with worsened markers of health, such as blood pressure control. It may be possible that the sampled patients turned to other sources for health maintenance and antihypertensive therapy during clinic closure. Future studies could explore these possibilities.
Scene wireframes sketching for Unmanned Aerial Vehicles

This paper introduces novel insights to improve the state-of-the-art line-based unsupervised observation and abstraction models of man-made environments. The increasing use of autonomous UAVs inside buildings and around human-made structures demands new accurate and comprehensive representation of their operation environments. Most of the 3D scene abstraction methods use invariant feature point matching, nevertheless some sparse 3D point clouds do not concisely represent the structure of the environment. The presented approach is based on observation and representation models using the straight line segments. The goal of the work is a complete method based on the matching of lines, that provides a complementary approach to state-of-the-art methods when facing 3D scene representation of poor texture environments for future autonomous UAV. Oppositely to other recently published methods obtaining 3D line abstractions, the proposed method features 3D segment abstraction in the absence of a previously generated point based reconstruction. Another advantage is the ability to group the resulting 3D lines according to different planes, for exploiting coplanar line intersections. These intersections are used like feature points in the reconstruction process. It has been proved that this method exclusively based on lines can obtain spatial information in the adverse situations when a SIFT-like SfM pipeline fails to generate a dense point cloud. (C) 2018 Elsevier Ltd. All rights reserved.
Microbial production of lactic acid: the latest development

Lactic acid is an important platform chemical for producing polylactic acid (PLA) and other value-added products. It is naturally produced by a wide spectrum of microbes including bacteria, yeast and filamentous fungi. In general, bacteria ferment C5 and C6 sugars to lactic acid by either homo- or hetero-fermentative mode. Xylose isomerase, phosphoketolase, transaldolase, l- and d-lactate dehydrogenases are the key enzymes that affect the ways of lactic acid production. Metabolic engineering of microbial strains are usually needed to produce lactic acid from unconventional carbon sources. Production of d-LA has attracted much attention due to the demand for producing thermostable PLA, but large scale production of d-LA has not yet been commercialized. Thermophilic Bacillus coagulans strains are able to produce l-lactic acid from lignocellulose sugars homo-fermentatively under non-sterilized conditions, but the lack of genetic tools for metabolically engineering them severely affects their development for industrial applications. Pre-treatment of agriculture biomass to obtain fermentable sugars is a pre-requisite for utilization of the huge amounts of agricultural biomass to produce lactic acid. The major challenge is to obtain quality sugars of high concentrations in a cost effective-way. To avoid or minimize the use of neutralizing agents during fermentation, genetically engineering the strains to make them resist acidic environment and produce lactic acid at low pH would be very helpful for reducing the production cost of lactic acid.
The dynamics of mercury around an artisanal and small-scale gold mining area, Camarines Norte, Philippines

To elucidate the dynamics of mercury emitted and released by artisanal and small-scale gold mining (ASGM) activity and to estimate its impact on the ecosystems of the bay, the distribution of mercury in the atmosphere, soil, water, and sediment around Mambulao Bay, Camarines Norte, Philippines, was investigated. The ASGM operations use mercury to extract gold from ore and are located on the east shore side of the bay. Samplings were conducted in August 2017 and September 2018. The samples were used for determination of total mercury (T-Hg) and organic mercury (org-Hg) concentrations, total organic carbon (TOC) content, and chemical composition. The atmospheric mercury concentration on the east shore side, 6.1-25.8 ng m-3, was significantly higher than the value of 1.4-9.9 ng m-3 observed on the west shore side. The average concentrations of T-Hg in the forest soils of the west shore side and those of the east shore side were 0.081 ± 0.028 mg kg-1 and 0.496 ± 0.439 mg kg-1, respectively. In the vertical distribution of T-Hg in the soil of the east shore side, a higher concentration was observed near the surface. For the vertical variations in T-Hg in the marine sediment, higher values were observed near the estuary, and the vertical variations in core samples showed an increase in mercury concentration toward the surface. The highest concentration of T-Hg in sediment, 9.5 mg kg-1, which was 2 orders of magnitude higher than the background levels of this area, was found near the river mouth. The T-Hg, org-Hg, and TOC levels showed a positive correlation, suggesting that the rivers are the main sources of T-Hg and org-Hg in the bay. Although the fish sample containing a mercury content higher than the regulatory level for fish and shellfish of 0.4 mg kg-1 in Japan was only one of 42 samples, the percentage of org-Hg in fish samples was 91 ± 18%. Mercury released into the surroundings by the ASGM activities can be converted into methylmercury and affect the bay's ecosystem.
Image Registration Based on Low Rank Matrix: Rank-Regularized SSD

Similarity measure is a main core of image registration algorithms. Spatially varying intensity distortion is an important challenge, which affects the performance of similarity measures. Correlation among the pixels is the main characteristic of this distortion. Similarity measures such as sum-of-squared-differences(SSD) and mutual information ignore this correlation; hence, perfect registration cannot be achieved in the presence of this distortion. In this paper, we model this correlation with the aid of the low rank matrix theory. Based on this model, we compensate this distortion analytically and introduce rank-regularized SSD (RRSSD). This new similarity measure is a modified SSD based on singular values of difference image in mono-modal imaging. In fact, image registration and distortion correction are performed simultaneously in the proposed model. Based on our experiments, the RRSSD similarity measure achieves clinically acceptable registration results, and outperforms other state-of-the-art similarity measures, such as the well-known method of residual complexity.
Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
Recursive estimation for sparse Gaussian process regression

Gaussian Processes (GPs) are powerful kernelized methods for non-parametric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs to larger datasets, several sparse approximations based on so-called inducing points have been proposed in the literature. In this work we investigate the connection between a general class of sparse inducing point GP regression methods and Bayesian recursive estimation which enables Kalman Filter like updating for online learning. The majority of previous work has focused on the batch setting, in particular for learning the model parameters and the position of the inducing points, here instead we focus on training with mini-batches. By exploiting the Kalman filter formulation, we propose a novel approach that estimates such parameters by recursively propagating the analytical gradients of the posterior over mini-batches of the data. Compared to state of the art methods, our method keeps analytic updates for the mean and covariance of the posterior, thus reducing drastically the size of the optimization problem. We show that our method achieves faster convergence and superior performance compared to state of the art sequential Gaussian Process regression on synthetic GP as well as real-world data with up to a million of data samples. (C) 2020 Elsevier Ltd. All rights reserved.
Continuous face authentication scheme for mobile devices with tracking and liveness detection

We present a novel scheme for continuous face authentication using mobile device cameras that addresses the issue of spoof attacks and attack windows in state-of-the-art approaches. Our scheme authenticates a user based on extracted facial features. However, unlike other schemes that periodically re-authenticate a user, our scheme tracks the authenticated face and only attempts re-authentication when the authenticated face is lost. This allows our scheme to eliminate attack windows that exist in schemes authenticating periodically and immediately recognise impostor usage. We also introduce a robust liveness detection component to our scheme that can detect printed faces and face videos. We describe how the addition of liveness detection enhances the robustness of our scheme against spoof attacks, improving on state-of-the-art approaches that lack this capability. Furthermore, we create the first dataset of facial videos collected from mobile devices during different real-world activities (walking, sitting and standing) such that our results reflect realistic scenarios. Our dataset therefore allows us to give new insight into the impact of user activity on facial recognition. Our dataset also includes spoofed facial videos for liveness testing. We use our dataset alongside two benchmark datasets for our experiments. We show and discuss how our scheme improves on existing continuous face authentication approaches and efficiently enhances device security.
A Study on the Factors Affecting Academic Achievement in the Non-Face-to-Face Class Environment Due to COVID-19: Focusing on Computer Liberal Arts Education Class

As a result of the COVID-19 pandemic, many universities have shifted to non-face-to-face classes resulting in numerous changes in the educational system. Since programming education includes a greater proportion of practice than theory-oriented courses, non-face-to-face classes have several constraints. As a result, to properly execute software education and enhance educational performance for non-major students, it is required to conduct research. Students' psychological moods and activities collected in online classrooms were used to investigate factors impacting academic success as measured by scores and grades. Multiple regression analysis and logistic regression analysis were conducted by using data mining analytical approach. Attendance, effort expectancy, hedonic motivation, confidence, frequency of communication in mobile chat rooms, and Python programming intention factors were retrieved as an outcome of the performance. The relevance of the factors was confirmed, and it was revealed that hedonic motivation was crucial for students in Class A, while attendance had a significant impact on academic progress for students in the other grades. The goal of this research is to assist university organizations in making decisions by enhancing computer liberal arts education and offering implications for future non-face-to-face teaching environments such as during the COVID-19 pandemic.
ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting

This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined graph structure for the complex spatial dependencies of traffic flow, and ineffectively harvest high dimensional temporal features of the traffic flow. In this paper, a novel multi-diffusion convolution block constructed by an attentive diffusion convolution and bidirectional diffusion convolution is proposed, which is capable to extract precise potential spatial dependencies. Moreover, a stacked Long Short-Term Memory (LSTM) block is adopted to capture high-dimensional temporal features. By integrating the two blocks, the ST-TrafficNet can learn the spatial-temporal dependencies of intricate traffic data accurately. The performance of the ST-TrafficNet has been evaluated on two real-world benchmark datasets by comparing it with three commonly-used methods and seven state-of-the-art ones. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) of the proposed method outperform not only the commonly-used methods, but also the state-of-the-art ones in 15 min, 30 min, and 60 min time-steps.
Development of a comprehensive two-dimensional liquid chromatographic mass spectrometric method for the non-targeted identification of poly- and perfluoroalkyl substances in aqueous film-forming foams

In this research, we developed an online comprehensive two-dimensional liquid chromatographic (LC × LC) method hyphenated with high-resolution mass spectrometry (HRMS) for the non-targeted identification of poly- and perfluorinated compounds (PFASs) in fire-fighting aqueous-film forming foams (AFFFs). The method exploited the combination of mixed-mode weak anion exchange-reversed phase with a octadecyl stationary phase, separating PFASs according to ionic classes and chain length. To develop and optimize the LC × LC method we used a reference training set of twenty-four anionic PFASs, representing the main classes of compounds occurring in AFFFs and covering a wide range of physicochemical properties. In particular, we investigated different modulation approaches to reduce injection band broadening and breakthrough in the second dimension separation. Active solvent and stationary phase assisted modulations were compared, with the best results obtained with the last approach. In the optimal conditions, the predicted peak capacity corrected for undersampling was higher than three-hundred in a separation space of about 60 min. Subsequently, the developed method was applied to the non-targeted analysis of two AFFF samples for the identification of homologous series of PFASs, in which it was possible to identify up to thirty-nine potential compounds of interest utilizing Kendrick mass defect analysis. Even within the samples, the features considered potential PFAS by mass defect analysis elute in the chromatographic regions discriminating for the ionic group and/or the chain length, thus confirming the applicability of the method presented for the analysis of AFFF mixtures and, to a further extent, of environmental matrices affected by the AFFF.
Early ontogeny of the commercially valuable fish red-bellied pacu Piaractus brachypomus (Characiformes, Serrasalmidae) from the Amazon, Brazil

The initial development of the red-bellied pacu Piaractus brachypomus is described using morphological, meristic and morphometric characteristics. A total of 127 individuals were analysed (47 in the yolk-sac, 35 in pre-flexion, five in flexion, 20 in post-flexion and 20 in juvenile) with standard length varying between 2.92 and 48.61 mm. The larvae are born poorly developed and have a discoidal yolk at ~6.33 mm standard length. During early ontogeny, the mouth passes from terminal to subterminal and the anal opening reaches the vertical line over the midline region of the body. There are changes in body shape from long and moderate to deep, head length from small to large, and eye diameter from moderate to large. Dendritic chromatophores were present in the ventral, dorsal and upper part of the swim bladder in the early larval stages. Rounded spots are evident all over the body in juveniles. The total number of myomeres ranges from 39 to 41 (20-23 pre-anal, 17-20 post-anal). Through the morphometric relationships, it was evidenced that the greatest changes during the initial ontogeny of P. brachypomus occur in the transition from the post-flexion stage to the juvenile period, indicating changes in behaviour, foraging and physiology.
Nanoemulsion-templated polylelectrolyte multifunctional nanocapsules for DNA entrapment and bioimaging

The emerging field of bionanotechnology aims at advancing colloidal and biomedical research via introduction of multifunctional nanoparticle-based containers intended for both gene therapy and bioimaging. In the present contribution we entrapped the model genetic material (herring testes DNA) in the newly-designed non-viral vectors, i.e., multifunctional nanocapsules obtained by layer-by-layer (LbL) adsorption of DNA and oppositely charged polysaccharide-based chitosan (CHIT) on the nanoemulsion core, loaded by IR-780 indocyanine (used as the fluorescent marker) and stabilized by gemini-type ammonium salts: N,N,N',N'-tetramethyl-N,N'-di(dodecyl)-ethylenediammonium bromide, d(DDA)PBr and N,N,N',N'-tetramethyl-N,N'-di(dodecyl)-butylenediammonium d(DDA)BBr. Ternary phase diagrams of the surfactant-oil-water systems were determined by titration method. Then, the stability of the nanoemulsions obtained with IR-780 solubilized in the oleic acid (OA) or isopropyl myristate (IPM) phase was evaluated by backscattering (BS) profiles and ζ-potential measurements. In the next step, CHIT and DNA layers were subsequently deposited on the kinetically stable nanoemulsion cores. The IR-780-loaded nanocarriers covered by (DNA/CHIT)4 bilayers shown the high ζ-potential value (about +43mV provided by Doppler electrophoresis), the size <120nm and the spherical shape as analyzed by dynamic light scattering (DLS), atomic force microscopy (AFM) and scanning electron microscopy (SEM). Finally, the long-lasting nanosystems were subjected to in vitro biological studies on human cancer cell lines - doxorubicin-sensitive breast (MCF-7/WT), epithelial lung adenocarcinoma (A549) and skin melanoma (MEWO). Biological response of the cell culture was expressed as cytotoxic activity evaluated by MTT-based proliferation assay as well as bioimaging of intracellular localization of IR-780 molecules loaded in the multilayer DNA-deposited nanocontainers - provided by confocal laser scanning microscopy (CLSM) and total internal reflection fluorescence microscopy (TIRFM). Our results demonstrate that the fabricated oil-core CHIT-coated nanocapsules stabilized by both d(DDA)PBr and d(DDA)BBr surfactants are promising as multifunctional nanocarriers for DNA delivery and cancer diagnostics.
NP-hardness of broadcast scheduling and inapproximability of single-source unsplittable min-cost flow

We consider the version of broadcast scheduling where a server can transmit W messages of a given set at each time-step. answering previously made requests for these messages. The goal is to minimize the average response time (ART) if the amount of requests is known in advance for each time-step and message. We prove that this problem is NP-hard, thus answering an open question stated by Kalyanasundaram, Pruhs and Velauthapillai (Proceedings of ESA 2000, LNCS 1879, 2000, pp. 290-301). Furthermore, we present an approximation algorithm that is allowed to send several messages at once. Using six channels for transmissions. the algorithm achieves an ART that is at least Lis good as the optimal solution using one channel. From the NP-hardness of broadcast scheduling we derive a new inapproximability result of (2 - epsilon, 1) for the (congestion. cost) bicriteria version of the single source unsplittable min-cost flow problem, for arbitrary epsilon > 0. The result holds even in the often considered case where the maximum demand is less than or equal to the minimum edge capacity (d(max) less than or equal to u(min)), a case for which an algorithm with ratio (3, 1) was presented by Skutella.