ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
CMOS DPDT switch at 2 GHz

A double-pole double-throw (DPDT) switch has been designed to operate from DC to 2 GHz. using a 0.18-mu m CMOS process. Each branch adopts series/shunt/series-type and large resistances art added to connect the bulks of series transistors to the ground to lower the insertion loss and increase the isolation. (c) 2006 Wiley Periodicals, Inc.
Food insecurity reported by children, but not by mothers, is associated with lower quality of diet and shifts in foods consumed

Household food security shows little indication of nutrient inadequacy among children, according to reports made by parents. We examined the associations of food insecurity as reported by children and mothers with children's consumption of energy, macronutrients such as vitamin A, calcium, iron and zinc, and selected foods, and whether these associations differed by child's gender. This cross-sectional study had non-probabilistic 128 Venezuelan mother-child pairs. We assessed food insecurity and management strategies in children using 10- and nine-item instruments, respectively. Mothers' report of food insecurity came from a previously validated 12-item instrument. Nutrient intake of children was assessed with a 67-item food frequency questionnaire. Comparisons were made using chi-square test for contingency tables and t-tests for trends (P < 0.05). Linear regression models were used for intakes of nutrients and selected foods. We tested for interactions with gender. Prevalence of child- and mother-reported food insecurity was 83.6 and 61.7%, respectively (P < 0.01). Greater food insecurity or management strategies reported by boys was associated with lower calcium, iron and zinc intake (P < 0.05), but reported intakes were low in girls who are even food secure. Rice and corn flour consumption was higher with higher food insecurity in children. Papaya and banana were less consumed by food-insecure children. We found shifts in 13 of 67 foods consumed, with less quality in those food insecure, as reported by children. Mother-reported food insecurity was associated only with rice intake of children. In contrast to mothers' reports, food insecurity reported by children was associated with children's lower quality of diet and shifts in foods consumed.
L-0-Regularized Intensity and Gradient Prior for Deblurring Text Images and Beyond

We propose a simple yet effective L-0-regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is based on distinctive properties of text images, with which we develop an efficient optimization algorithm to generate reliable intermediate results for kernel estimation. The proposed algorithm does not require any heuristic edge selection methods, which are critical to the state-of-the-art edge-based deblurring methods. We discuss the relationship with other edge-based deblurring methods and present how to select salient edges more principally. For the final latent image restoration step, we present an effective method to remove artifacts for better deblurred results. We show the proposed algorithm can be extended to deblur natural images with complex scenes and low illumination, as well as non-uniform deblurring. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art image deblurring methods.
Contour detection refined by a sparse reconstruction-based discrimination method

Sparse representations have been widely used for many image processing tasks. In this paper, a sparse reconstruction-based discrimination (SRBD) method, which was previously proposed for the classification of image patches, is utilized to improve boundary detection in colour images. This method is applied to refining the results generated by three different algorithms: a biologically inspired method, and two state-of-the-art algorithms for contour detection. All of the contour detection results are evaluated by the BSDS300 and BSDS500 benchmarks using the quantitative measures: F-score, ODS, OIS and AP. Evaluation results shows that the performance of each algorithm is improved using the proposed method of refinement with at least one of the quantitative measures increased by 0.01. In particularly, even two state-of-the-art algorithms are slightly improved by applying the SRBD method to refine their contour detection results.
Materialization of interactive stereoscopic artwork based on hand-painted images

This paper presents interactive stereoscopic artwork and an algorithm for natural artistic expression using hand-painted images expressed by the artist's manual brush strokes. The system proposes a new interactive method that allows a viewer to experience the painting process representing the consecutive process of an actual artist's oil painting. The combination of analog and digital techniques stimulates emotions of the audience. The system architecture is composed of the Kinect sensor, which recognizes the movement of the user, a module that generates real-time stereoscopic images, and a projection module that displays the generated image. The survey is conducted to evaluate the effects of the 3D modeling method and the artistic modeling method. The statistical result show that the proposed hand-painted method provides more artistic satisfaction to the viewers than the 3D modeling method.
A Don't-Care-Based Approach to Reducing the Multiplicative Complexity in Logic Networks

Reducing the number of AND gates in logic networks benefits the applications in cryptography, security, and quantum computing. This work proposes a don't-care-based (DC-based) approach to reduce the number of AND gates further in the well-optimized network. Furthermore, this work also proposes an enhanced synthesis flow by integrating our approach with the state-of-the-art. The experimental results show that our approach can further reduce up to 25% of the number of AND gates in the network. For the experiments about the enhanced synthesis flow, we achieve a speedup of almost 10x on average for the cryptography benchmarks while having competitive results as compared to the flow in the state-of-the-art.
Computing Crowd Consensus with Partial Agreement

Crowdsourcing has been widely established as a means to enable human computation at large-scale, in particular for tasks that require manual labelling of large sets of data items. Answers obtained from heterogeneous crowd workers are aggregated to obtain a robust result. However, existing methods for answer aggregation are designed for discrete tasks, where answers are given as a single label per item. In this paper, we consider partial-agreement tasks that are common in many applications such as image tagging and document annotation, where items are assigned sets of labels. Common approaches for the aggregation of partial-agreement answers either (i) reduce the problem to several instances of an aggregation problem for discrete tasks or (ii) consider each label independently. Going beyond the state-of-the-art, we propose a novel Bayesian nonparametric model to aggregate the partial-agreement answers in a generic way. This model enables us to compute the consensus of partially-sound and partially-complete worker answers, while taking into account mutual relationships in labels and different answer sets. We also show how this model is instantiated for incremental learning, incorporating new answers from crowd workers as they arrive. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of precision, recall, and robustness against faulty workers and data sparsity.
A Dynamic Zoom ADC With 109-dB DR for Audio Applications

This paper presents the first dynamic zoom ADC. Intended for audio applications, it achieves 109-dB DR, 106-dB signal-to-noise ratio, and 103-dB SNDR in a 20-kHz bandwidth, while dissipating only 1.12 mW. This translates into the stateof- the-art energy efficiency as expressed by a Schreier FoM of 181.5 dB. It also achieves the state-of-the-art area efficiency, occupying only 0.16 mm(2) in the 0.16-mu m CMOS. These advances are enabled by the use of concurrent fine and coarse conversions, dynamic error-correction techniques, and a dynamically biased inverter-based operational transconductance amplifier.
Two Case Reports of Patients With Transverse Myelitis as a Complication of SARS-CoV-2 Infection

Transverse myelitis is a nontraumatic spinal cord injury that presents with sudden onset weakness, sensory deficits, and autonomic dysfunction. It can be caused by multiple etiologies including malignancy, autoimmune disorders, viral, bacterial, or fungal infections, and environmental factors. In this article, we describe cases of two elderly male patients affected by the SARS-CoV-2 virus. Patients did not exhibit classic or had only mild classic symptoms of SARS-CoV-2 infection; however, both patients developed transverse myelitis. Patients were treated with intravenous steroids and therapeutic plasmapheresis, achieving partial improvement. The study aimed to understand rare complications like transverse myelitis of SARS-CoV-2 infection and treatment accordingly.
Leveraging Auxiliary Information on Marginal Distributions in Nonignorable Models for Item and Unit Nonresponse

Often, government agencies and survey organizations know the population counts or percentages for some of the variables in a survey. These may be available from auxiliary sources, for example, administrative databases or other high quality surveys. We present and illustrate a model-based framework for leveraging such auxiliary marginal information when handling unit and item nonresponse. We show how one can use the margins to specify different missingness mechanisms for each type of nonresponse. We use the framework to impute missing values in voter turnout in a subset of data from the U.S. Current Population Survey (CPS). In doing so, we examine the sensitivity of results to different assumptions about the unit and item nonresponse.
Cellular type of hemangioblastoma. Case report and literature review

The authors report a patient with spinomedullary tumor who underwent resection with subsequent histological examination. However, the authors encountered difficulties in determining the exact histological type of neoplasm. Microscopic and immunohistochemical examination of spinomedullary neoplasm revealed two types of tumor: ependymoma and hemangioblastoma. However, analysis of literature data indicated that the identified tumor could be attributed to a certain cellular type of hemangioblastoma.
Accelerated and Concerted Aza-Michael Addition and SuFEx Reaction in Microdroplets in Unitary and High-Throughput Formats

The sulfur fluoride exchange (SuFEx) reaction is significant in drug discovery, materials science, and chemical biology. Conventionally, it involves installation of SO2 F followed by fluoride exchange by a catalyst. We report catalyst-free Aza-Michael addition to install SO2 F and then SuFEx reaction with amines, both occurring in concert, in microdroplets under ambient conditions. The microdroplet reaction is accelerated by a factor of ∼104 relative to the corresponding bulk reaction. We suggest that the superacidic microdroplet surface assists SuFEx reaction by protonating fluorine to create a good leaving group. The reaction scope was established by performing individual reactions in microdroplets of 18 amines in four solvents and confirmed using high-throughput desorption electrospray ionization experiments. The study demonstrates the value of microdroplet-assisted accelerated reactions in combination with high-throughput experimentation for characterization of reaction scope.
Optimum sizing of the inverter for maximizing the energy yield in state-of-the-art high-concentrator photovoltaic systems

The sizing of the inverter in comparison to the rated capacity of the photovoltaic generator is investigated for high-concentrator photovoltaic (HCPV) systems. An HCPV module of typical characteristics is modelled and parameterized, taking into account direct normal irradiance (DNI), ambient temperature, air mass and aerosol optical depth as atmospheric inputs, while the DC losses of the HCPV generator are allowed to vary in the ranges reported in the literature. A set of 80 commercial inverters are analysed to obtain the typical efficiency curves of state-of-the-art low-, medium-, and high-efficiency inverters. Four locations worldwide with high annual DNI levels and different average values of the weather variables influencing HCPV performance are studied. Results show that the inverter can be sized between 84% and 112% of the rated capacity of the HCPV generator at Concentrator Standard Test Conditions depending on the scenario considered for maximizing the final energy yield of the system. The proposed methodology uses analytical equations, all the model parameters are provided and justified and atmospheric inputs are obtained from meteorological databases in order to make the application easy regarding its use in other locations where the climate data is available.
Correlation-Based Tracker-Level Fusion for Robust Visual Tracking

Although visual object tracking algorithms are capable of handling various challenging scenarios individually, none of them are robust enough to handle all the challenges simultaneously. For any online tracking by detection method, the key issue lies in detecting the target over the whole frame and updating systematically a target model based on the last detected appearance to avoid the drift phenomenon. This paper aims at proposing a novel robust tracking algorithm by fusing the frame level detection strategy of tracking, learning, & detection with the systematic model update strategy of Kernelized Correlation Filter tracker. The risk of drift is mitigated by the fact that the model updates are primarily driven by the detections that occur in the spatial neighborhood of the latest detections. The motivation behind the selection of trackers is their complementary nature in handling tracking challenges. The proposed algorithm efficiently combines the two state-of-the-art tracking algorithms based on conservative correspondence measure with strategic model updates, which takes advantages of both and outperforms them on their short ends by virtue of other. Extensive evaluation of the proposed method based on different metrics is carried out on the data sets ALOV300++, Visual Tracker Benchmark, and Visual Object Tracking. We demonstrated its performance in terms of robustness and success rate by comparing with state-of-the-art trackers.
Tipping the balance toward stemness in trophoblast: Metabolic programming by Cox6B2

Metabolic effector(s) driving cell fate is an emerging concept in stem cell biology. Here we showed that Cytochrome C Oxidase Subunit 6B2 (Cox6B2) is essential to maintain the stemness of trophoblast stem (TS) cells. RNA interference of Cox6b2 resulted in decreased mitochondrial Complex IV activity, ATP production, and oxygen consumption rate in TS cells. Furthermore, depletion of Cox6b2 in TS cells led to decreased self-renewal capacity indicated by compromised BrdU incorporation, Ki67 staining, and decreased expression of TS cell genetic markers. As expected, the consequence of Cox6b2 knockdown was the induction of differentiation. TS cell stemness factor CDX2 transactivates Cox6b2 promoter in TS cells. In differentiated cells, Cox6b2 is post-transcriptionally regulated by two microRNAs, miR-322-5p and miR-503-5p, leading to its downregulation as demonstrated by the gain-in or loss of function of these miRNAs. Cox6b2 transcripts gradually rise in placental trophoblast gestation progresses in both mice and rats with predominant expression in labyrinthine trophoblast. Cox6b2 expression is compromised in the growth-restricted placenta of rats with reciprocal up-regulation of miR-322-5p and miR-503-5p. These data highlight the importance of Cox6B2 in the regulation of TS cell state and uncompromised placental growth.
Vehicular Communication Systems: Enabling Technologies, Applications, and Future Outlook on Intelligent Transportation

Numerous technologies have been deployed to assist and manage transportation. But recent concerted efforts in academia and industry point to a paradigm shift in intelligent transportation systems. Vehicles will carry computing and communication platforms, and will have enhanced sensing capabilities. They will enable new versatile systems that enhance transportation safety and efficiency and will provide infotainment. This article surveys the state-of-the-art approaches, solutions, and technologies across a broad range of projects for vehicular communication systems.
Carbon nanotubes supported tyrosinase in the synthesis of lipophilic hydroxytyrosol and dihydrocaffeoyl catechols with antiviral activity against DNA and RNA viruses

Hydroxytyrosol and dihydrocaffeoyl catechols with lipophilic properties have been synthesized in high yield using tyrosinase immobilized on multi-walled carbon nanotubes by the Layer-by-Layer technique. All synthesized catechols were evaluated against a large panel of DNA and RNA viruses, including Poliovirus type 1, Echovirus type 9, Herpes simplex virus type 1 (HSV-1), Herpes simplex virus type 2 (HSV-2), Coxsackievirus type B3 (Cox B3), Adenovirus type 2 and type 5 and Cytomegalovirus (CMV). A significant antiviral activity was observed in the inhibition of HSV-1, HSV-2, Cox B3 and CMV. The mechanism of action of the most active dihydrocaffeoyl derivative was investigated against a model of HSV-1 infection.
Combining Individual Travel Preferences Into Destination Prediction: A Multi-Module Deep Learning Network

Accurate destination prediction over sub-trajectories is essential for a wide range of location-based services. Traditional trip matching methods fail to capture temporal dependence hidden in trajectories and may suffer from data sparsity problems. With the help of massive trajectory data, state-of-the-art approaches based on deep learning (DL) have achieved great success. However, existing DL approaches rarely consider the influence of individual travel preferences in destination prediction. When the trip is long but the known partial trajectory is short, DL models are unable to produce satisfactory results. Thus, we design a feature extraction mechanism to extract useful temporal features, spatial features, and static covariates for destination prediction, among which the spatial features characterize individual travel preferences by considering two main movement patterns in daily travel. Then, a hierarchical model including multiple modules is proposed to finely process heterogeneous features. Extensive experiments conducted on two public datasets demonstrate the superior performance of the proposed model compared to the state-of-the-art methods. Moreover, further experimental results show that the proposed model still performs well when trajectory prefix is short or travel duration is long, which confirms the effectiveness of integrating individual travel preferences.
Industry-Fit AI Usage for Crack Detection in Ground Steel

We investigated optimal implementation strategies for industrial inspection systems aiming to detect cracks on ground steel billets' surfaces by combining state-of-the-art AI-based methods and classical computational imaging techniques. In 2D texture images, the interesting patterns of surface irregularities are often surrounded by visual clutter, which is to be ignored, e.g., grinding patterns. Even neural networks struggle to reliably distinguish between actual surface disruptions and irrelevant background patterns. Consequently, the image acquisition procedure already has to be optimised to the specific application. In our case, we use photometric stereo (PS) imaging to generate 3D surface models of steel billets using multiple illumination units. However, we demonstrate that the neural networks, especially in high-speed scenarios, still suffer from recognition deficiencies when using raw photometric stereo camera data, and are unable to generalise to new billets and image acquisition conditions. Only the additional application of adequate state-of-the-art image processing algorithms guarantees the best results in both aspects. The neural networks benefit when appropriate image acquisition methods together with image processing algorithms emphasise relevant surface structures and reduce overall pattern variation. Our proposed combined strategy shows a 9.25% better detection rate on validation data and is 14.7% better on test data, displaying the best generalisation.
Vasoactive Intestinal Peptide Tumor as the Cause of Persistent Diarrhea: A Diagnostic Challenge

Although chronic diarrhea is frequent, some of its causes are rare, namely, neuroendocrine tumors (NETs). Due to their rarity and non-specific symptoms, such as diarrhea, they are often underdiagnosed. An 80-year-old woman presented to the emergency department due to emesis and watery diarrhea. Blood tests showed acute kidney injury, hypokalemia, and metabolic acidosis. An abdominal computed tomography revealed a 51 mm pancreatic lesion. An endoscopic ultrasound-guided biopsy raised the hypothesis of a NET. The patient refused surgery and was lost to follow-up. At the eighth hospitalization, 11 months later, the suspicion of a vasoactive intestinal peptide tumor (VIPoma) was raised and confirmed by assessing the vasoactive intestinal peptide (VIP) levels (>100 pmol/L). Octreotide was started with the resolution of the symptoms. 68Ga-DOTANOC positron emission tomography/computed tomography excluded metastatic disease. After six months of octreotide therapy, the tumor shrunk 13 mm in maximum diameter. At the last follow-up, one year later, she remained asymptomatic. The delayed diagnosis of VIPoma led to multiple life-threatening episodes. This case highlights the importance of considering all potential differential diagnoses of common symptoms such as diarrhea. Although VIPomas are rare, clinicians should be aware of this entity and suspect this diagnosis in patients with chronic diarrhea with a poor response to standard antidiarrheal agents. Somatostatin analogs should be promptly prescribed for symptom control and tumor progression prevention in patients who refuse surgery or have unresectable tumors. Tumor shrinkage might also be observed in these cases.
The relationship between past exercise behavior and future exercise adherence: A sequential mediation analysis

The present study explored the mediation role of past exercise adherence, self-reported frequency and intentions in the association between past experience and future exercise adherence. In total, 431 exercisers (female = 216; male = 215) aged 18 and 64 years, engaged in fitness activities such as group fitness classes and resistance training, were included in the analysis. Serial mediation procedures were employed to examine the direct, indirect, and total indirect effects among variables. The predictor variable and all mediators displayed a positive and significant association with future six-month adherence. Past six-month exercise adherence displayed the most significant association with future six-month adherence. The sequential indirect path from exercise experience → past six-months adherence → self-reported frequency → intentions future six-months adherence displayed a positive and significant effect (β = .19 [CI95% = .09, .31]), presenting a partial mediation effect. Past behaviour is the most significant predictor of future adherence, and thus interventions should be based on promoting consistent exercise frequency. Professionals working in the fitness centre context can identify possible dropouts based on their past behaviour and intentions to be physically active in the future.