ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Providers' reflections on infrastructure and improvements to promote access to care for Veterans experiencing housing instability in rural areas of the United States: A qualitative study

Veterans in rural areas of the United States face barriers to accessing healthcare and other services, which are intensified for those experiencing housing instability. Recent legislative acts have the potential to address obstacles faced by rural patients in the U.S. This study explores how infrastructure-including features related to the physical and digital environment-impacts the ability of rural Veterans experiencing housing instability to access healthcare and related services from the perspective of homeless service providers within the Veterans Health Administration (VHA). We conducted semi-structured telephone interviews (n = 22) with providers in high/low performing and/or resourced communities across the U.S. in May and June 2021 and analysed transcripts using template analysis. Themes described by providers highlight how infrastructure limitations in rural areas can exacerbate health disparities for Veterans experiencing housing instability, the impact of COVID-19 on service access, and recommendations to enhance service delivery. Providers suggested that VHA reconfigure where and how staff work, identify additional resources for transportation and/or alternative transportation models, and increase Veterans' access to technology and broadband Internet. Federal infrastructure investments should address challenges faced by Veterans experiencing housing instability in rural areas and the concerns of providers connecting them with care.
Ovarian vein thrombosis: A rare cause of abdominal pain as a complication of an elective abortion

Ovarian vein thrombosis (OVT) is a rare diagnosis. Patients can appear to be very uncomfortable on presentation with a physical examination that can mimic an acute abdomen. OVT is most often diagnosed during the postpartum period [Jenayah et al., 2015] and not typically seen during pregnancy or after procedures such as dilation and curettage (D&C). The complications from an OVT are significant and include sepsis, thrombophlebitis and pulmonary embolism [Harris et al., 2012]. Here we describe a case of OVT with an atypical presentation, diagnosed twenty-four hours after an elective D&C for a second trimester abortion.
High efficiency DCM buck converter with dynamic logic based adaptive switch-time control

This paper presents a dynamic logic based adaptive switch-time control circuit (DASTC) for fast control to minimize body diode conduction losses and dead-time of a buck regulator operating in discontinuous conduction mode. Dead-time is an important metric for improving efficiency of low voltage converters. DASTC provides instant sensing and feedback based on the load to minimize dead-time significantly compared to prior-art and scales low side power switch pulse width adaptively across load currents, just enough to discharge the stored inductor energy fully. Furthermore, the converter has inherent pulse skipping mode to lower the switching frequency at light loads to enhance light load efficiency. The proposed 1.5 V buck regulator was fabricated in 0.35 mu m CMOS process with an input voltage range of 1.8-3 V and load current range of 1-200 mA. Measurement results demonstrate a peak power efficiency of 94.6 % and a high overall power efficiency (>similar to 89 %) for load currents greater than 5 mA for V-IN = 1.8 V. The proposed scheme achieves 4x (similar to 5 ns) better dead-time and 4-5.5 % better power efficiency compared to prior-art over the load current range.
iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations

In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.
Adversarial scratches: Deployable attacks to CNN classifiers

A growing body of work has shown that deep neural networks are susceptible to adversarial examples. These take the form of small perturbations applied to the model's input which lead to incorrect predictions. Unfortunately, most literature focuses on visually imperceivable perturbations to be applied to digital images that often are, by design, impossible to be deployed to physical targets. We present Adversarial Scratches: a novel L-0 black-box attack, which takes the form of scratches in images, and which possesses much greater deployability than other state-of-the-art attacks. Adversarial Scratches leverage Bezier Curves to reduce the dimension of the search space and possibly constrain the attack to a specific location. We test Adversarial Scratches in several scenarios, including a publicly available API and images of traffic signs. Results show that our attack achieves higher fooling rate than other deployable state-of-the-art methods, while requiring significantly fewer queries and modifying very few pixels. (C) 2022 Elsevier Ltd. All rights reserved.
Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision

With the growth of autonomous vehicles and collision-avoidance systems, several approaches using deep learning and convolutional neural networks (CNNs) continually address accuracy improvement in obstacle detection. The authors introduce a three-stage architecture that adds side channels as low-level features to serve as input to existing CNNs. In a case study, the architecture is used to extract depth from stereo cameras, and then compose RGBD inputs to state-of-the-art CNNs to improve their vehicle and pedestrian detection accuracy. This can be achieved by simple modifications on the first layers of any existing CNN with RGB inputs. To validate the architecture, the state-of-the-art matching cost-CNN, and cascade residual learning, both specialist algorithms to extract depth information combined to the state-of-the-art Faster-region-based CNN, MSCNCN, and Subcategory-aware Convolutional Neural Network (SubCNN) to yield the models to be tested using the KITTI dataset benchmark. In many cases, the accuracy (in terms of average precision) using their proposal outperforms the original scores in various scenarios of detection difficulty, reaching improvements up to +3.96% in the training and +1.50% in the testing KITTI datasets. This proposal also introduces efficient methods to initialise the weights of the depth convolutional filters during transfer learning using net surgery.
A new ART-counterpropagation neural network for solving a forecasting problem

This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer). ART-CPN involves real-time learning and is capable of developing a more stable and plastic prediction model of input patterns by self-organization. The advantages of ART-CPN include the ability to cluster, learn and construct the network model for forecasting problems. The network was applied to solve the real forecasting problems. The learning algorithm revealed better learning efficiency and good prediction performance. (C) 2004 Elsevier Ltd. All rights reserved.
Effects of predator modulation and vector preference on pathogen transmission in plant populations

Biological control programs frequently rely on predators to control vector-borne pathogens by consumptive effects on vector abundance in agroecosystems. Meanwhile, the spread of vectored disease depends on the vector preference for host status (healthy or infected hosts). Yet, it is unclear how vector preferences alter the controlled effectivity of predators in pathogen transmission. Therefore, we here addressed the plant-vector-pathogen models assessing how pathogen transmission in plant was affected by variable predation rates and vector preferences for host status. Specifically, we discussed effects of predators on vector abundance and pathogen transmission under both a non-spatial model and a spatially structured metapopulation model. We showed that predators can decrease the vector abundance and inhibit pathogen prevalence, whereas vector preference contributes profoundly to the controlled effectivity of predators on the spread of vector-borne pathogens. Moreover, predation can increase oscillation amplitude of the pathogen prevalence in both plant and vector; suggesting that the inclusion of predator can amplify the effects of environmental stochasticity on pathogen dynamics. In conclusion, our results support the prediction of theoretical disease models showing predator can be a natural enemy for pathogen control, and also extend that predatory interactions interacting with vector preferences play the singularly joint effects on the spread of vector-borne pathogens.
Markedly lowering the viscosity of aqueous solutions of DNA by additives

Aqueous solutions of DNAs, while relevant in drug delivery and as a target of therapies, are often very viscous making them difficult to use. Since less viscous solutions could enable targeted drug delivery and/or therapies, the purpose of the present work was to explore compounds capable of "thinning" such DNA solutions under pharmaceutically relevant conditions. To this end, viscosities of aqueous solutions of DNAs and model polyanions were examined at 25 °C in the absence and presence of a number of bulky organic salts (and related compounds) previously found to substantially lower the viscosities of concentrated protein solutions. Out of two dozen compounds tested, only three were found to be effective; the FDA-approved local anesthetics lidocaine, mepivacaine, and prilocaine at near-isotonic concentrations and pH 6.4 lowered solution viscosity of three different DNAs up to about 20 fold. The observed multi-fold viscosity reductions appear to be due to these bulky organic salts' structure-specific non-covalent binding to nucleotide bases resulting in denaturation (unwinding) to, and stabilization of, single-stranded DNA.
A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions

Occupational health and safety (OHS) is a multidisciplinary activity working under the tasks of protection of workers and worksites. Risk assessment, as a compulsory process in implementation of OHS, stands out as evaluating the risks arising from the hazards, taking into account the required control measures, and deciding whether or not the risks can be reduced to an acceptable level. The diversity in risk assessment approaches is such that there are many methods for any industry. Multicriteria decision-making (MCDM)-based approaches contribute to risk assessment knowledge with their ability on solving real-world problems with multiple, conflicting, and incommensurate criteria. This article conducts a critical state-of-the-art review of OHS risk assessment studies using MCDM-based approaches. Additionally, it includes fuzzy versions of MCDM approaches applied to OHS risk assessment. A total of 80 papers are classified in eight different application areas. The papers are reviewed by the points of publication trend, published journal, risk parameters/factors, and tools used. This critical review provides an insight for researchers and practitioners on MCDM-based OHS risk assessment approaches in terms of showing current state and potential areas for attempts to be focused in the future.
Multilingual Audio-Visual Smartphone Dataset and Evaluation

Smartphones have been employed with biometric-based verification systems to provide security in highly sensitive applications. Audio-visual biometrics are getting popular due to their usability, and also it will be challenging to spoof because of their multimodal nature. In this work, we present an audio-visual smartphone dataset captured in five different recent smartphones. This new dataset contains 103 subjects captured in three different sessions considering the different real-world scenarios. Three different languages are acquired in this dataset to include the problem of language dependency of the speaker recognition systems. These unique characteristics of this dataset will pave the way to implement novel state-of-the-art unimodal or audio-visual speaker recognition systems. We also report the performance of the bench-marked biometric verification systems on our dataset. The robustness of biometric algorithms is evaluated towards multiple dependencies like signal noise, device, language and presentation attacks like replay and synthesized signals with extensive experiments. The obtained results raised many concerns about the generalization properties of state-of-the-art biometrics methods in smartphones.
Warping of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive Applications

We present an approach to automatically generate semantic labels for real recordings of automotive range-Doppler (RD) radar spectra. Such labels are required when training a neural network for object recognition from radar data. The automatic labeling approach rests on the simultaneous recording of camera and lidar data in addition to the radar spectrum. By warping radar spectra into the camera image, state-of-the-art object recognition algorithms can be applied to label relevant objects, such as cars, in the camera image. The warping operation is designed to be fully differentiable, which allows backpropagating the gradient computed on the camera image through the warping operation to the neural network operating on the radar data. As the warping operation relies on accurate scene flow estimation, we further propose a novel scene flow estimation algorithm which exploits information from camera, lidar and radar sensors. The proposed scene flow estimation approach is compared against a state-of-the-art scene flow algorithm, and it outperforms it by approximately 30% w.r.t. mean average error. The feasibility of the overall framework for automatic label generation for RD spectra is verified by evaluating the performance of neural networks trained with the proposed framework for Direction-of-Arrival estimation.
Mitigation of radiation exposure during surgical hepatectomy after yttrium-90 radioembolization

Yttrium-90 (Y-90) radioembolization for the treatment of hepatocellular carcinoma can present safety challenges when transplanting recently treated Y-90 patients. To reduce surgeons' contact with radioactive tissue and remain within occupational dose limits, current guidelines recommend delaying transplants at least 14 days, if possible. We wanted to determine the level of radiation exposure to the transplant surgeon when explanting an irradiated liver before the recommended decay period. An ex-vivo radiation exposure analysis was conducted on the explanted liver of a patient who received Y-90 therapy 46 h prior to orthotopic liver transplant. To estimate exposure to the surgeon's hands, radiation dosimeter rings were placed inside three different surgical glove configurations and exposed to the explanted liver. Estimated radiation doses corrected for Y-90 decay were calculated. Radiation safety gloves performed best, with an average radiation exposure rate of 5.36 mSV h(-1) in the static hand position, an 83% reduction in exposure over controls with no glove (31.31 mSv h(-1)). Interestingly, non-radiation safety gloves also demonstrated reduced exposure rates, well below occupational regulation limits. Handling of Y-90 radiated organs within the immediate post-treatment period can be done safely and does not exceed federal occupational dose limits if appropriate gloves and necessary precautions are exercised.
Cross-Bar Design of Nano-Vacuum Triode for High-Frequency Applications

In this letter, a new nano-vacuum triode based on carbon nanotubes (CNTs) has been designed. The use of CNTs as emitters with their extremely high aspect ratio and their characteristics to be patterned in specific emitting areas allowed the realization of a cross-bar geometry for which the transconductance is maximized and the grid-cathode capacitance is reduced. This allowed us to achieve a device cutoff frequency of 156 GHz, which is well beyond the state of the art.
Multi-Scale Pathological Fluid Segmentation in OCT With a Novel Curvature Loss in Convolutional Neural Network

The segmentation of pathological fluid lesions in optical coherence tomography (OCT), including intraretinal fluid, subretinal fluid, and pigment epithelial detachment, is of great importance for the diagnosis and treatment of various eye diseases such as neovascular age-related macular degeneration and diabetic macular edema. Although significant progress has been achieved with the rapid development of fully convolutional neural networks (FCN) in recent years, some important issues remain unsolved. First, pathological fluid lesions in OCT show large variations in location, size, and shape, imposing challenges on the design of FCN architecture. Second, fluid lesions should be continuous regions without holes inside. But the current architectures lack the capability to preserve the shape prior information. In this study, we introduce an FCN architecture for the simultaneous segmentation of three types of pathological fluid lesions in OCT. First, attention gate and spatial pyramid pooling modules are employed to improve the ability of the network to extract multi-scale objects. Then, we introduce a novel curvature regularization term in the loss function to incorporate shape prior information. The proposed method was extensively evaluated on public and clinical datasets with significantly improved performance compared with the state-of-the-art methods.
Unconventional localization of electrons inside of a nematic electronic phase

The magnetotransport behavior inside the nematic phase of bulk FeSe reveals unusual multiband effects that cannot be reconciled with a simple two-band approximation proposed by surface-sensitive spectroscopic probes. In order to understand the role played by the multiband electronic structure and the degree of two-dimensionality, we have investigated the electronic properties of exfoliated flakes of FeSe by reducing their thickness. Based on magnetotransport and Hall resistivity measurements, we assess the mobility spectrum that suggests an unusual asymmetry between the mobilities of the electrons and holes, with the electron carriers becoming localized inside the nematic phase. Quantum oscillations in magnetic fields up to 38 T indicate the presence of a hole-like quasiparticle with a lighter effective mass and a quantum scattering time three times shorter, as compared with bulk FeSe. The observed localization of negative charge carriers by reducing dimensionality can be driven by orbitally dependent correlation effects, enhanced interband spin fluctuations, or a Lifshitz-like transition, which affect mainly the electron bands. The electronic localization leads to a fragile two-dimensional superconductivity in thin flakes of FeSe, in contrast to the two-dimensional high-[Formula: see text] induced with electron doping via dosing or using a suitable interface.
THE ARTISTIC PLANNING METHOD OF URBAN GARDEN PLANT LANDSCAPE UNDER THE CONCEPT OF ECOLOGICAL SUSTAINABLE DEVELOPMENT

Landscape planning is difficult for designers to be influenced by bad culture. It is easy to ignore the cultural progress of the times and lose the sense of innovation. At the same time, in order to pursue the quick effect, the movement of "transplanting big trees", "lawn hot" and "urban beautification" prevails leads to the problems of regional ecological environment. Therefore, this paper puts forward a kind of urban landscape plant landscape art under the concept of ecological sustainable development based on the improving the plant growth environment, protecting, and transmitting the ecological environment and the participation of the public in the design. At the same time, the design follows the ecological design principles of locality, protection of diversity and natural, social, and sustainable plant landscapes. Finally, the "beauty, greening, and natural" artistry is incorporated into the design to complete the plan for the concept of sustainable development, reflecting the "coordinated" and "comprehensive" beauty of garden plant landscape art.
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of "Deep Learning" strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.