ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Erythrocyte fatty acid profiles and plasma homocysteine, folate and vitamin B6 and B12 in recurrent depression: Implications for co-morbidity with cardiovascular disease

Oxidative stress induced interactions between fatty acid (FA) and one-carbon metabolism may be involved in co-occurrence of major depressive disorder (MDD) and cardiovascular disease (CVD), which have been scarcely studied together. In 137 recurrent MDD-patients vs. 73 age- and sex-matched healthy controls, we simultaneously measured key components of one-carbon metabolism in plasma (homocysteine, folate, vitamins B6 and B12), and of FA-metabolism in red blood cell membranes [main polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and arachidonic acid (AA) and structural FA-indices (chain length, unsaturation, peroxidation)]. Results show significant positive associations of folate with EPA, DHA, and the peroxidation index, which were similar in patients and controls. After correction for confounders, these associations were lost except for EPA. Associations between B-vitamins and FA-parameters were non-significant, but also similar in patients and controls. Homocysteine and DHA were significantly less negatively associated in patients than in controls. In conclusion, these data indicate similarities but also differences in associations between parameters of one-carbon and FA-metabolism in recurrent MDD patients vs. controls, which may reflect differences in handling of oxidative stress. Further research should test the consequences of these differences, particularly the premature development of CVD in MDD.
Helicobacter pylori-Negative Gastric Mucosa-Associated Lymphoid Tissue Lymphoma Presenting as Massive Gastrointestinal Bleed

This case reports a patient that represents the minority of patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma who do not have underlying Helicobacter pylori gastritis. Gastric MALT lymphoma is a type of primary gastric lymphoma (PGL), which are extremely rare gastric malignancies characterized by proliferation of B-cells and infiltration of lymphoid tissue leading to destruction of gastric glands. Development of gastric MALT lymphoma is associated with H. pylori gastritis. Patients typically present with a wide range of symptoms including but not limited to epigastric pain, weight loss, gastrointestinal bleeding and gastric wall perforation. Gastric MALT lymphoma presenting as a massive gastrointestinal bleed is quite rare and only a few cases have been documented. Our case demonstrates that it is important to recognize that acute presentations of this disease may also occur.
Viral Suppression and Resistance in a Cohort of Perinatally-HIV Infected (PHIV plus ) Pregnant Women

Our objective was to describe viral suppression and antiretroviral (ARV) resistance mutations in an ongoing cohort of perinatally-infected HIV+ (PHIV+) pregnant women. Descriptive analysis was performed using SPSS 18.0. From 2011 to 2014, we followed 22 PHIV+ pregnant women. Median age at prenatal entry was 19 years (Interquartile range (IQR) 17.6-21.0); 86% had an AIDS diagnosis; 81% had disclosed their HIV status to partner 11. The median age at HIV diagnosis was 8.3 y (IQR 4.0-13.6), the median age at sexual debut was 16 years (IQR 14-18). At the time of prenatal care initiation, four (18%) were on their first antiretroviral treatment (ART), eight (36%) in their second regimen and nine (41%) in their third regimen or beyond, and one had no data. Seventeen of 22 (77%) had HIV-viral load (VL) > 50 copies/mL at prenatal care entry, 16 had a genotyping exam performed. Seventeen of 22 PHIV+ had VL results near delivery: 7/17 (41%) had VL < 50 copies/mL. Among those who had genotyping at prenatal entry, 11/16 (69%) had mutations associated with ARV resistance. The most frequent major mutations were K103N, M184V, T215, M41L, D67N at reverse transcriptase gene and M46, I54V and V82A at protease gene. No vertical transmissions occurred. Management of pregnancy among PHIV+ is challenging. Individualized ART are needed to achieve viral suppression in a highly ART-exposed subpopulation.
A Fully-Parallel Turbo Decoding Algorithm

This paper proposes a novel alternative to the Logarithmic Bahl-Cocke-Jelinek-Raviv (Log-BCJR) algorithm for turbo decoding, yielding significantly improved processing throughput and latency. While the Log-BCJR processes turbo-encoded bits in a serial forwards-backwards manner, the proposed algorithm operates in a fully-parallel manner, processing all bits in both components of the turbo code at the same time. The proposed algorithm is compatible with all turbo codes, including those of the LTE and WiMAX standards. These standardized codes employ odd-even interleavers, facilitating a novel technique for reducing the complexity of the proposed algorithm by 50%. More specifically, odd-even interleavers allow the proposed algorithm to alternate between processing the odd-indexed bits of the first component code at the same time as the even-indexed bits of the second component, and vice-versa. Furthermore, the proposed fully-parallel algorithm is shown to converge to the same error correction performance as the state-of-the-art turbo decoding algorithm. Owing to its significantly increased parallelism, the proposed algorithm facilitates throughputs and latencies that are up to 6.86 times superior to those of the state-of-the art algorithm, when employed for the LTE and WiMAX turbo codes. However, this is achieved at the cost of a moderately increased computational complexity and resource requirement.
Research on garment pattern design based on fractal graphics

This paper firstly analyzes the basic principle of generating fractal art graphics and the artistic features of graphics and then uses scientific visualization method to innovate and improve the theoretical model used in this paper. The generation principle and graphic characteristics of fractal graphics of complex dynamic system and L-system are mainly analyzed, and two kinds of art graphicsflower art graphics and geometric art graphicshave been developed. On this basis, the generated artistic figures are designed for the second time and then applied to the design of clothing patterns. By using MATLAB programming software to generate art graphics conforming to a specific style, combined with image processing software Photoshop to process and redesign the generated graphics, these art graphics can assist the design of clothing printing patterns and make patterns applicable for clothing fabrics. Finally, the fractal pattern theory is applied to silk scarves design and clothing fabric design through digital printing technology, which can fully reflect the practicability and superiority of clothing pattern design based on the fractal theory. Based on the experimental result, it shows that it is completely feasible to design clothing fabric printing patterns based on fractal theory, and the unusual artistic patterns designed have very considerable practical value. In addition, this method encourages creativity in the garment pattern design process and accelerates new design generation.
Relational Fusion Networks: Graph Convolutional Networks for Road Networks

The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road networks. We introduce the Relational Fusion Network (RFN), a novel type of Graph Convolutional Network (GCN) designed specifically for road networks. In particular, we propose methods that outperform state-of-the-art GCN architectures by up to 21-40% on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs may fail to effectively leverage road network structure and may not generalize well to other road networks.
Geochemical accumulation and source tracing of heavy metals in arable soils from a black shale catchment, southwestern China

Heavy metal enrichment in soils has been linked to the weathering of lithologies with high geochemical backgrounds, such as black shale. Therefore, this study conducted a typical sampling of surface soils in a black shale catchment in southwestern China to characterize the accumulation and sources of the heavy metals As, Cd, Co, Cr, Cu, Ni, Pb, Zn, Mo and Tl. Elevated concentrations of most heavy metals in the soils underlain by black shale are determined to exceed the regional soil background values, even the risk screening values, especially for Mo, As and Cd. Sequential extraction analysis, together with previous results, reveals that most heavy metals in soils are mainly bound in the residual fraction (> 65 %) as a result of the fixation of stable aluminosilicates (e.g., clay minerals). In contrast, Cd mainly occurs in relatively labile proportions as exchangeable (24.42 %), carbonate (24.48 %) and Fe/Mn oxide fractions (26.60 %) due to the non-specific adsorption of soil colloids and the precipitation of carbonates and Fe/Mn oxides. Pb isotopic tracing and APCS/MLR receptor model suggest that heavy metals in the urban surface soils (SG1) have a mixed source of black shale weathering, vehicle exhaust and agricultural input, while heavy metals in the rural surface soils (SG2) are a geogenic source of black shale weathering. Overall, this study provides new insights into contamination management, land use planning and health risk assessment in regions with high geochemical backgrounds.
SAW Tags With Enhanced Penetration Depth for Buried Assets Identification (Aug. 2020)

To identify buried assets, this article presents a novel design of surface acoustic wave (SAW) tag to enhance its underground penetration capacity. A penetration depth model for a SAW tag buried in materials is established to investigate the depth of detection and guide the design of the SAW tag. Multipulse position with all reflectors in two groups (MPP-ART) coding scheme is proposed to reduce the insertion loss of the SAW chip. A novel embedded monopole microstrip tag antenna is investigated with a stable frequency performance when embedded in diverse types of materials. Furthermore, a linear frequency modulation pulse compression transceiver based on software defined radio is implemented to enhance the penetration depth. The insertion loss of the SAW chip at 920 MHz with the MPP-ART coding scheme, is reduced to 26.58 dB. Through experiments, the penetration depth of the SAW radio frequency identification prototype has reached 0.75 m in river water, 0.8 m in tap water, 1.5 m in wet sand, and 3 m in dry sand, respectively. The experimental results demonstrate that the SAW tag can work in various materials with good penetration depth, relatively immune to electromagnetic interference with adjacent metallic objects, making it suitable for the identification of buried assets for industrial applications.
TM-generation model: a template-based method for automatically solving mathematical word problems

In this study, we propose a novel model called template-based multitask generation (TM-generation) that can improve the problem-solving accuracy of mathematical word problem-solving task. In automatic mathematical word problem-solving task, a machine learning model should deduce an answer to a given problem by acquiring implied numeric information. To build a robust model that can sufficiently utilize numeric information to solve various mathematical word problems, such a model should address two challenges: (1) filling in missing world knowledge required to solve the given mathematical word problem, and (2) understanding the implied relationship between numbers and variables. To address these two challenges, we propose template-based multitask generation (TM-generation). To address challenge (1), we utilize the state-of-the-art language models called ELECTRA. To address challenge (2), we propose an operator identification layer that models the relationship between numbers and variables. Our experimental results show that using the MAWPS and Math23k datasets, state-of-the-art performance was achieved: 85.2% in MAWPS and 85.3% in Math23k.
Low-Latency Software Polar Decoders

Polar codes are a new class of capacity-achieving error-correcting codes with low encoding and decoding complexity. Their low-complexity decoding algorithms rendering them attractive for use in software-defined radio applications where computational resources are limited. In this work, we present low-latency software polar decoders that exploit modern processor capabilities. We show how adapting the algorithm at various levels can lead to significant improvements in latency and throughput, yielding polar decoders that are suitable for high-performance software-defined radio applications on modern desktop processors and embedded-platform processors. These proposed decoders have an order of magnitude lower latency and memory footprint compared to state-of-the-art decoders, while maintaining comparable throughput. In addition, we present strategies and results for implementing polar decoders on graphical processing units. Finally, we show that the energy efficiency of the proposed decoders is comparable to state-of-the-art software polar decoders.
ASSESSMENT OF THE RISK FOR DEVELOPING A SECOND MALIGNANCY FROM SCATTERED AND SECONDARY RADIATION IN RADIATION THERAPY

With the average age of radiation therapy patients decreasing and the advent of more complex treatment options comes the concern that the incidences of radiation-induced cancer might increase in the future. The carcinogenic effects of radiation are not well understood for the entire dose range experienced in radiation therapy. Longer epidemiologic studies are needed to improve current risk models and reduce uncertainties of current risk model parameters. On the other hand, risk estimations are needed today to judge the risks versus benefits of modern radiation therapy techniques. This paper describes the current state-of-the-art in risk modeling for radiation-induced malignancies in radiation therapy, distinguishing between two volumes: first, the organs within the main radiation field receiving low or intermediate doses (typically between 0.1 and 50 Gy); and second, the organs far away from the treatment volume receiving low doses mainly due to scattered and secondary radiation (typically below 0.1 Gy). The dosimetry as well as the risk model formalisms are outlined. Furthermore, example calculations and results are presented for intensity-modulated photon therapy versus proton therapy. Health Phys. 103(5):652-661; 2012
Parameterized Strain Estimation for Vascular Ultrasound Elastography With Sparse Representation

Ultrasound vascular strain imaging has shown its potential to interrogate the motion of the vessel wall induced by the cardiac pulsation for predicting plaque instability. In this study, a sparse model strain estimator (SMSE) is proposed to reconstruct a dense strain field at a high resolution, with no spatial derivatives, and a high computation efficiency. This sparse model utilizes the highly-compacted property of discrete cosine transform (DCT) coefficients, thereby allowing to parameterize displacement and strain fields with truncated DCT coefficients. The derivation of affine strain components (axial and lateral strains and shears) was reformulated into solving truncated DCT coefficients and then reconstructed with them. Moreover, an analytical solution was derived to reduce estimation time. With simulations, the SMSE reduced estimation errors by up to 50% compared with the state-of-the-art window-based Lagrangian speckle model estimator (LSME). The SMSE was also proven to be more robust than the LSME against global and local noise. For in vitro and in vivo tests, residual strains assessing cumulated errors with the SMSE were 2 to 3 times lower than with the LSME. Regarding computation efficiency, the processing time of the SMSE was reduced by 4 to 25 times compared with the LSME, according to simulations, in vitro and in vivo results. Finally, phantom studies demonstrated the enhanced spatial resolution of the proposed SMSE algorithm against LSME.
Impact of adherence to hydroxyurea on health outcomes among patients with sickle cell disease

Although new pharmaceutical therapy options have recently become available, hydroxyurea is still the most commonly used and affordable treatment option for sickle cell disease (SCD). This study aimed to update the evidence on hydroxyurea adherence and its association with clinical and economic outcomes among individuals with SCD. This retrospective study used Texas Medicaid claims data from 09/2011-08/2016. Individuals were included if they had ≥1 inpatient or ≥2 outpatient SCD diagnoses, had ≥1 hydroxyurea prescription, were 2-63 years of age, and were continuously enrolled in Texas Medicaid between 6 months before and 1 year after the first hydroxyurea prescription fill date (index date). Hydroxyurea adherence (Medication Possession Ratio; MPR), vaso-occlusive crisis (VOC)-related outcomes, healthcare utilization and expenditures (SCD-related and all-cause) during the 1 year following the index date were measured. Bivariate and multivariable analyses were used to address the study objectives. Among 1035 included individuals (age: 18.8 ± 12.5 years, female: 52.1%), 20.9% were adherent to hydroxyurea (defined as MPR≥0.8). After adjustment for demographic and clinical characteristics, compared to being non-adherent, adhering to hydroxyurea was significantly associated with: a lower risk (Odds Ratio [OR] = 0.480, p = .0007) and hazard rate (Hazard Ratio [HR] = 0.748, p = .0005) of a VOC event, fewer VOC events (Incidence Rate Ratio [IRR] = 0.767, p = .0009), fewer VOC-related hospital days (IRR = 0.593, p = .0003), fewer all-cause and SCD-related hospitalizations (IRR = 0.712, p = .0008; IRR = 0.707, p = .0008, respectively) and emergency department visits (IRR = 0.768, p = .0037; IRR = 0.746, p = .0041, respectively), and lower SCD-related total healthcare expenditures (IRR = 0.796, p = .0266). Efforts to increase adherence to hydroxyurea could improve clinical and economic outcomes among individuals with SCD.
On the design and operation of primary settling tanks in state of the art wastewater treatment and water resources recovery

In state of the art wastewater treatment, primary settling tanks (PSTs) are considered as an integral part of the biological wastewater and sludge treatment process, as well as of the biogas and electric energy production. Consequently they strongly influence the efficiency of the entire wastewater treatment plant. However, in the last decades the inner physical processes of PSTs, largely determining their efficiency, have been poorly addressed. In common practice PSTs are still solely designed and operated based on the surface overflow rate and the hydraulic retention time (HRT) as a black box. The paper shows the results of a comprehensive investigation programme, including 16 PSTs. Their removal efficiency and inner physical processes (like the settling process of primary sludge), internal flow structures within PSTs and their impact on performance were investigated. The results show that: (1) the removal rates of PSTs are generally often underestimated in current design guidelines, (2) the removal rate of different PSTs shows a strongly fluctuating pattern even in the same range of the HRT, and (3) inlet design of PSTs becomes highly relevant in the removal efficiency at rather high surface overflow rates, above 5 m/h, which is the upper design limit of PSTs for dry weather load.
SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases

Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of state-of-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19.
Residue buildup predictive modeling for stencil cleaning profile decision-making using recurrent neural network

This research proposes a novel framework to control the stencil cleaning profile selection in the stencil printing process (SPP). The SPP is a major contributor to yield loss in surface mount technology (SMT). Enhancement in SPP performance is critical to improving the printed circuit board (PCB) assembly line. The selection of a solvent-based or a dry-based cleaning profile is challenging, but the choice determines the effectiveness and efficiency of the stencil cleaning operation. The amount of residue buildup under the stencil is the main criterion used to decide the appropriate cleaning profile in SPP. In this research, a multi-dimensional temporal recurrent neural network (RNN) approach is used to accurately predict the amount of residue buildup on the underneath surface of the stencil in real-time. Specifically, the long short-term memory (LSTM) architecture is trained using actual residue buildup data. The proposed LSTM prediction model is compared with other state-of-the-art regression models such as multilayer perceptron (MLP) and ensemble learning models. Experimental results show the proposed LSTM model outperforms the state-of-the-art regression models and accurately predicts the stencil status. The proposed research aids decision-makers in the SPP line to select the appropriate stencil cleaning profile adaptively and in real-time. As a result, the overall SPP performance is improved.