Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches ...are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation. To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future.
In the present work, we demonstrate the fabrication of laccase biosensor to detect the catechol (CC) using laccase immobilized on graphene-cellulose microfibers (GR-CMF) composite modified screen ...printed carbon electrode (SPCE). The direct electrochemical behavior of laccase was investigated using laccase immobilized different modified SPCEs, such as GR/SPCE, CMF/SPCE and GR-CMF/SPCE. Compared with laccase immobilized GR and CMF modified SPCEs, a well-defined redox couple of Cu
/Cu
for laccase was observed at laccase immobilized GR-CMF composite modified SPCE. Cyclic voltammetry results show that the as-prepared biosensor has 7 folds higher catalytic activity with lower oxidation potential towards CC than SPCE modified with GR-CMF composite. Under optimized conditions, amperometric i-t method was used for the quantification of CC, and the amperometric response of the biosensor was linear over the concertation of CC ranging from 0.2 to 209.7 μM. The sensitivity, response time and the detection limit of the biosensor for CC is 0.932 μMμA
cm
, 2 s and 0.085 μM, respectively. The biosensor has high selectivity towards CC in the presence of potentially active biomolecules and phenolic compounds. The biosensor also accessed for the detection of CC in different water samples and shows good practicality with an appropriate repea.
Artifacts in Digital Breast Tomosynthesis Geiser, William R; Einstein, Samuel A; Yang, Wei-Tse
American journal of roentgenology (1976),
10/2018, Letnik:
211, Številka:
4
Journal Article
Recenzirano
Artifacts in digital breast tomosynthesis and synthesized 2D imaging reduce image quality. This article describes the appearance of these unique artifacts, reviews their causes, and discusses methods ...to ameliorate these artifacts.
Artifacts in digital breast tomosynthesis and synthesized 2D imaging can obscure important findings on mammograms. The radiologist, mammography technologist, and medical physicist must be able to recognize these artifacts and use the vendor's new processing algorithms to mitigate the effects of such artifacts.
Transition metal complexes offer cost-effective alternatives as hole-transport materials (HTMs) in perovskite solar cells. However, the devices suffer from low performance. We boost the power ...conversion efficiency of devices with transition metal complex HTMs from 2% to above 10% through energy level tuning. We further demonstrate the excellent photostability of the device based on the additive-free HTM.
We developed a high-performance hole transport material based on transition metal complexes for perovskite solar cells, which exhibits excellent photostability.
Neoadjuvant chemotherapy is becoming the standard of care for patients with locally advanced breast cancer. Conventional imaging modalities used for the assessment of tumor response to neoadjuvant ...chemotherapy rely on changes in size or morphologic characteristics and, therefore, are inherently limited.
Functional imaging technologies evaluate vascular, metabolic, biochemical, and molecular changes in cancer cells and have a unique ability to detect specific biologic tumor markers, assess therapeutic targets, predict early response to neoadjuvant chemotherapy, and guide individualized cancer therapy.
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A simple and cost effective synthesis of nanomaterials with advanced physical and chemical properties have received much attention to the researchers, and is of interest to the ...researchers from different disciplines. In the present work, we report a simple and one pot electrochemical synthesis of poly(melamine) entrapped gold nanoparticles (PM-AuNPs) composite. The PM-AuNPs composite was prepared by a single step electrochemical method, wherein the AuNPs and PM were simultaneously fabricated on the electrode surface. The as-prepared materials were characterized by various physicochemical methods. The PM-AuNPs composite modified electrode was used as an electrocatalyst for oxidation of catechol (CC) due to its well-defined redox behavior and enhanced electro-oxidation ability towards CC than other modified electrodes. Under optimized conditions, the differential pulse voltammetry (DPV) was used for the determination of CC. The DPV response of CC was linear over the concentration ranging from 0.5 to 175.5μM with a detection limit of 0.011μM. The PM-AuNPs composite modified electrode exhibits the high selectivity in the presence of range of potentially interfering compounds including dihydroxybenzene isomers. The sensor shows excellent practicality in CC containing water samples, which reveals the potential ability of PM-AuNPs composite modified electrode towards the determination of CC in real samples.
Crystalline hydroxyapatite were synthesized from synthetic/human urine through precipitation which were further doped with silver nanoparticle for effective biomedical application. The aim were to ...improve overall biological compatibility of the synthesized bone-graft material even in oncogenesis cases. The thermal calcinated material was characterized by several techniques including UV–vis, Laser Raman, Fourier transmittance infrared spectroscopy, X-ray diffraction analysis, Transmission Eelectron microscopy and X-ray fluorescence spectroscopy. The quantitative and qualitative analysis revealed that the synthesized material was highly crystalline and nanosized with majority of silver and phosphate components. The antibacterial, anticancer and invitro cytotoxicity of the synthesized material was evaluated with Escherichia coli, Hela cells and brine shrimp assay, respectively. The brine shrimp assay revealed that the synthesized material is compatible with biological system, whereas anticancer activity showed the application of the synthesized biomaterial in cancer treatment in which antibacterial activity adds more advantage on preventing the bone-graft from microbial attack.
The purpose of this study was to evaluate the clinical, imaging, and histopathologic findings of primary neuroendocrine carcinoma of the breast.
A pathology database was searched for the records of ...patients with a histopathologic diagnosis of primary neuroendocrine carcinoma of the breast who had undergone mammography, sonography, or MRI between 1984 and 2011. The imaging studies of eligible patients were retrospectively reviewed according to the BI-RADS lexicon, and clinical presentation and histopathologic characteristics were documented. Imaging characteristics were compared with historical controls of invasive mammary carcinoma.
Eighty-seven patients (84 women, three men; mean age, 62.9 years; range, 28-89 years) were included in the study. The mean tumor size was 3.1 cm (range, 0.6-11 cm). Sixty-five of 84 (77.4%) cancers were estrogen and progesterone receptor positive and ERBB2 negative. A palpable mass (55.8%) was a common clinical manifestation. A high-density, round or oval, or lobular mass with nonspiculated margins on mammograms and an irregular (65.4%), hypoechoic (78.4%) mass, with indistinct margins (43.5%), no or enhanced posterior acoustic features (77.9%) on sonograms were common findings. MRI revealed an irregular mass (83.3%), irregular margins (63.6%), and washout kinetics (85.7%). Neuroendocrine carcinoma presented more frequently as masses on mammograms. Calcifications were infrequent compared with their occurrence in invasive mammary cancer.
Primary neuroendocrine carcinoma of the breast has mammographic features that differ from those of invasive mammary carcinoma. A round, oval, or lobular mass with nonspiculated margins, positive estrogen and progesterone receptor results, and negative ERBB2 results should raise suspicion of primary neuroendocrine carcinoma.
Design rule checking (DRC) violation (DRV) prediction with early stage design information can help to reduce the iterations of design procedure and can speed up the physical-design closure. It is ...known that accurately predicting detailed routing-level DRV with information obtained at global route (GR) stage can significantly speed up the design closure. However, without sufficient prediction accuracy, the result may lead to suboptimal design or even longer design time. Therefore, in this article, we propose two machine-learning frameworks to predict the detailed routing-level DRV map of a given design. The first framework is based on the congestion report obtained at global routing stage, and the second framework considers both the placement information and the congestion report of global routing. We then compare the runtime and accuracy of the two models. The proposed frameworks utilize convolutional neural network as the core technique to train these prediction models. The training dataset is collected from 15 industrial designs using a leading commercial automatic placement and routing (APR) tool, and the total number of collected training samples exceeds 26M. A specialized under-sampling technique is also proposed to select important training samples for learning, compensate for the inaccuracy misled by a highly imbalanced training dataset, and speed up the entire training process. The experimental results demonstrate that our both models can result in not only a significantly higher accuracy than previous related works, but also a DRV map visually matching the actual ones closely. The average runtime of using our learned model from the first framework to generate a DRV map is only 3% of global routing, and the prediction accuracy of our learned model from the second framework can improve 7.6% compared to the one from the first framework. Our proposed framework can be viewed as a simple add-on tool to a current commercial placement and global router that can efficiently and effectively generate a more realistic DRV map without really applying detailed routing.
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