Notes

Simultaneous resolution of triazine herbicides and their metabolites inside seafood by HPLC-MS/MS joined with Q/E-Orbitrap HRMS.
The value of this new labeled approach is demonstrated in retrospective data from 5 patients. Results show that, by including a series of 5 untracked images in time, a single LUS image can be registered with accuracies ranging from 5.7 to 16.4 mm with a success rate of 78%. Initialisation of the LUS to CT registration with the proposed framework could potentially enable the clinical translation of these image fusion techniques.A spatial resolution metric is presented for tomosynthesis. The Fourier spectral distortion metric (FSD) was developed to evaluate specific resolution properties of different imaging techniques for digital tomosynthesis using a star pattern image to plot modulation in the frequency domain. The FSD samples the spatial resolution of a star-pattern image tangentially over an acute angle and for a range of spatial frequencies in a 2D image or 3D image reconstruction slice. The FSD graph portrays all frequencies present in a star pattern quadrant. In addition to the fundamental input frequency of the star pattern, the FSD graph shows spectral leakage, square wave harmonics, and residual noise. The contrast transfer function (CTF) is obtained using the FSD graph. The CTF is analogous to the modulation transfer function (MTF), but it is not normalized to unity at zero spatial frequency. AG-1024 datasheet Unlike the MTF, this metric separates the fundamental input-frequency from the other signals in the Fourier domain. This metric helps determine optimal image reconstruction parameters, the in-plane limit of spatial resolution with respect to aliased signals, and a threshold criterion for an image to support super resolution and reduce aliasing artifacts. Various sampling parameters were evaluated to optimize this metric and ascertain measurement accuracy. The FSD adequately compares resolution properties of 2D images and 3D image reconstruction slices for various x ray imaging modes without suppressing aliased signals.Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system, in this paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly plugged into any networks as sampling, upsampling and downsampling function. On account of its efficient features representation ability, networks can achieve competitive results with only several level blocks. Second, it's observed that traditional autoencoder can only learn an ambiguous model that also reconstructs anomalies "well" due to lack of constraints in training and inference process. To mitigate this challenge, we design a hash addressing memory module that proves abnormalities to produce higher reconstruction error for classification. In addition, we couple the mean square error (MSE) with Wasserstein loss to improve the encoding data distribution. Experiments on various datasets, including two different COVID-19 datasets and one brain MRI (RIDER) dataset prove the robustness and excellent generalization of the proposed MAMA Net.Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging procedure is inevitably contaminated by heavy speckle noise, which impairs both visual quality and diagnosis of various ocular diseases. Although deep learning has been applied for image denoising and achieved promising results, the lack of well-registered clean and noisy image pairs makes it impractical for supervised learning-based approaches to achieve satisfactory OCT image denoising results. In this paper, we propose an unsupervised OCT image speckle reduction algorithm that does not rely on well-registered image pairs. Specifically, by employing the ideas of disentangled representation and generative adversarial network, the proposed method first disentangles the noisy image into content and noise spaces by corresponding encoders. Then, the generator is used to predict the denoised OCT image with the extracted content features. In addition, the noise patches cropped from the noisy image are utilized to facilitate more accurate disentanglement. Extensive experiments have been conducted, and the results suggest that our proposed method is superior to the classic methods and demonstrates competitive performance to several recently proposed learning-based approaches in both quantitative and qualitative aspects. Code is available at https//github.com/tsmotlp/DRGAN-OCT.Despite the success of convolutional neural network (CNN) in conventional closed-set recognition (CSR), it still lacks robustness for dealing with unknowns (those out of known classes) in open environment. To improve the robustness of CNN in open-set recognition (OSR) and meanwhile maintain its high accuracy in CSR, we propose an alternative deep framework called convolutional prototype network (CPN), which keeps CNN for representation learning but replaces the closed-world assumed softmax with an open-world oriented and human-like prototype model. To equip CPN with discriminative ability for classifying known samples, we design several discriminative losses for training. Moreover, to increase the robustness of CPN for unknowns, we interpret CPN from the perspective of generative model and further propose a generative loss, which is essentially maximizing the log-likelihood of known samples and serves as a latent regularization for discriminative learning. The combination of discriminative and generative losses makes CPN a hybrid model with advantages for both CSR and OSR. Under the designed losses, the CPN is trained end-to-end for learning the convolutional network and prototypes jointly. For application of CPN in OSR, we propose two rejection rules for detecting different types of unknowns. Experiments on several datasets demonstrate the efficiency and effectiveness of CPN for both CSR and OSR tasks.
A number of movement intent decoders exist in the literature that typically differ in the algorithms used and the nature of the outputs generated. Each approach comes with its own advantages and disadvantages. Combining the estimates of multiple algorithms may have better performance than any of the individual methods.

This paper presents and evaluates a shared controller framework for prosthetic limbs based on multiple decoders of volitional movement intent.

An algorithm to combine multiple estimates to control the prosthesis is developed in this paper. The capabilities of the approach are validated using a system that combines a Kalman filter-based decoder with a multilayer perceptron classifier-based decoder. The shared controller's performance is validated in online experiments where a virtual limb is controlled in real-time by amputee and intact-arm subjects. During the testing phase subjects controlled a virtual hand in real time to move digits to instructed positions using either a Kalman filter sed decoder, resulting in a system that may be able to perform the tasks of everyday life more naturally and reliably.
In current surface acoustic wave (SAW) elastography field, wavelength-depth inversion model is a straightforward and widely used inversion model for depth-resolved elasticity profile reconstruction. However, the elasticity directly evaluated from the wavelength-depth relationship is biased. Thus, a new inversion model, termed weighted average phase velocity (WAPV) inversion model, is proposed to provide depth-resolved Young's modulus estimate with better accuracy.

The forward model for SAW phase velocity dispersion curve generation was derived from the numerical simulations of SAWs in layered materials, and inversion was implemented by matching the measured phase velocity dispersion curve to the one generated from the forward model using the least squares fitting. Three two-layer agar phantoms with different top-layer thicknesses and one three-layer agar phantom were tested to validate the proposed inversion model. Then the model was demonstrated on human skin at various sites (palm, forearm and back of hand) in-vivo.

In multi-layered agar phantoms, depth-resolved elasticity estimates provided by the model have a maximal total inversion error of 15.2% per sample after inversion error compensation. In in-vivo human skin, the quantified bulk Young's moduli (palm 212 ± 78 kPa; forearm 32 ± 11 kPa and back of hand 29 ± 8 kPa) are comparable to the reference values in the literature.

The WAPV inversion model can provide accurate depth-resolved Young's modulus estimates in layered biological soft tissues.

The proposed model can predict depth-resolved elasticity in layered biological soft tissues with a reasonable accuracy which traditional wavelength-depth inversion model cannot provide.
The proposed model can predict depth-resolved elasticity in layered biological soft tissues with a reasonable accuracy which traditional wavelength-depth inversion model cannot provide.For more than half a century, oral anticoagulant and antiplatelet therapy has been used to decrease the risk of thromboembolism, prolonging the lives of countless patients. Patients taking antithrombotic agents may be at risk of excessive hemorrhage. Dentists commonly see such patients, and this can pose a challenge, as adequate hemostasis is crucial for the success of invasive dental treatment. Many dentists refer these patients, as they lack understanding or fear uncontrollable bleeding during and after surgery. In this clinical review, we discuss the mechanisms of hemostasis, drugs that can interfere with these pathways and how to safely and effectively manage patients who are taking antithrombotic agents. We include which procedures are considered safe, which are riskier in terms of bleeding, what laboratory tests must be reviewed before treatment, drug interactions with commonly prescribed dental drugs, as well as agents that can aid in hemostasis. Although antithrombotics cause an increase in bleeding, there is general consensus that treatment regimens should not be altered before routine dental procedures when the risk of bleeding is moderate to low. Procedures that require drug alterations include extractions of more than 3 teeth, crown lengthening, open-flap surgery, surgical extractions and periodontal surgery.
Early childhood caries (ECC) originates prenatally. This study investigated whether a relation exists between levels of vitamin D in the umbilical cord and caries in offspring.

A prospective cohort of expectant mothers was selected from a high-risk urban population receiving prenatal care in Winnipeg, Canada. Participants self-selected into 1 of 2 groups. The intervention group received 2 oral prenatal doses of 50 000 international units (IU) of vitamin D in addition to routine prenatal care. The control group received routine prenatal care. A prenatal questionnaire was completed at the first visit. Umbilical cord blood was analyzed for 25 hydroxyvitamin D (25(OH)D). At the time of their infant's first birthday, participants returned for a follow-up questionnaire and a dental examination of the infant. A p value ≤ 0.05 was significant.

In all, 283 women were recruited (mean age 23.4 ± 5.6 years), 141 in the intervention group and 142 in the control group. The mean cord 25(OH)D level was 49.6 ± 24.3 nmol/L and did not differ between the groups.
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