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Placental Studies inside Preterm along with Time period Preeclampsia: An Integrative Overview of the actual Novels.
Identifying cancer subtypes holds essential promise for improving prognosis and personalized treatment. Cancer subtyping based on multi-omics data has become a hotspot in bioinformatics research. One of the critical approaches of handling data heterogeneity in multi-omics data is first modeling each omics data as a separate similarity graph. Then, the information of multiple graphs is integrated into a unified graph. However, a significant challenge is how to measure the similarity of nodes in each graph and preserve cluster information of each graph. To that end, we exploit a new high order proximity in each graph and propose a similarity fusion method to fuse the high order proximity of multiple graphs while preserving cluster information of multiple graphs. Compared with the current techniques employing the first order proximity, exploiting high order proximity contributes to attaining accurate similarity. The proposed similarity fusion method makes full use of the complementary information from multi-omics data. Experiments in six benchmark multi-omics datasets and two individual cancer case studies confirm that our proposed method achieves statistically significant and biologically meaningful cancer subtypes.This research article reports the electrical detection of breast-cancer biomarker (C-erbB-2) in saliva/serum based on In1-xGaxAs/Si heterojunction dopingless tunnel FET (HJ-DL-TFET) biosensor for highly sensitive and real-time detection. The work takes into account the interface charge modulation effect in dopingless extended gate heterostructure TFET with embedded nanocavity biosensors for the precise, reliable, and fast detection of antigens present in the body fluids such as saliva in place of blood serum. The reported biosensor is numerically simulated in 2D using the SILVACO ATLAS exhaustive calibrated simulation framework. For the biomolecule immobilization, the proposed biosensor has a dual cavity engraved beneath the dual gate structure. This improves the control of biomolecules over the source-to-channel tunneling rate, as well as the control over the electrical performance parameters of the proposed biosensor. Here, a numerical model for the C-erbB-2 interface charge equivalent is also developed. The analysis of device sensitivity in both saliva and serum environments for various C-erbB-2 concentrations has been carried out. Our study reveals that III-V In1-xGaxAs/Si heterojunction with x composition of 0.2 and extended gate geometry provides an increased tunneling probability, improves the gate control to get a higher ION/IOFF ratio and higher sensitivity. In addition to this, the impact of interface charges corresponding to the different amounts of C-erbB-2 biomarkers on the biosensor sensitivity (in terms of ION/IOFF ratio) yields higher sensitivity of the order of 106.The quiet standing test is used to detect diseases of the postural control system. The descriptive statistics of the center of pressure (COP) of older people during the test tend to be larger than those of healthy young people, but they cannot indicate structural problems in postural control. COP trajectories can be mathematically modeled with structural parameters such as viscosity, stiffness, and stochastic terms; however, the classification accuracy of older and fall-experienced people using such parameters has not been sufficiently verified. In this study, six structural parameters of a mass-spring-damper (MSD) model were estimated using two datasets, in which a total of 212 subjects performed quiet standing tests under four conditions. The estimated parameters were used for classification with a random forest algorithm to examine the differences in classification accuracy compared to seven conventional descriptive statistics methods. For the classification of older subjects, the classification accuracy of the MSD parameter method was the highest in foam condition, with positive likelihood ratios approximately 8.0. For the classification of fall-experienced subjects, the positive likelihood ratio of the MSD parameter method was 5.0, which is better than conventional descriptive statistics. Various MSD parameters revealed that aging and changing the floor surface and visual conditions cause oscillations in the COP behavior. While the MSD parameters were confirmed to help classify older subjects more accurately than the conventional descriptive statistics, there was room for further improvement in the classification accuracy of fall-experienced subjects.Completing missing entries in multidimensional visual data is a typical ill-posed problem that requires appropriate exploitation of prior information of the underlying data. NSC16168 concentration Commonly used priors can be roughly categorized into three classes global tensor low-rankness, local properties, and nonlocal self-similarity (NSS); most existing works utilize one or two of them to implement completion. Naturally, there arises an interesting question can one concurrently make use of multiple priors in a unified way, such that they can collaborate with each other to achieve better performance? This work gives a positive answer by formulating a novel tensor completion framework which can simultaneously take advantage of the global-local-nonlocal priors. In the proposed framework, the tensor train (TT) rank is adopted to characterize the global correlation; meanwhile, two Plug-and-Play (PnP) denoisers, including a convolutional neural network (CNN) denoiser and the color block-matching and 3 D filtering (CBM3D) denoiser, are incorporated to preserve local details and exploit NSS, respectively. Then, we design a proximal alternating minimization algorithm to efficiently solve this model under the PnP framework. Under mild conditions, we establish the convergence guarantee of the proposed algorithm. Extensive experiments show that these priors organically benefit from each other to achieve state-of-the-art performance both quantitatively and qualitatively.Composed Query Based Image Retrieval (CQBIR) aims at retrieving images relevant to a composed query containing a reference image with a requested modification expressed via a textual sentence. Compared with the conventional image retrieval which takes one modality as query to retrieve relevant data of another modality, CQBIR poses great challenge over the semantic gap between the reference image and modification text in the composed query. To solve the challenge, previous methods either resort to feature composition that cannot model interactions in the query or explore inter-modal attention while ignoring the spatial structure and visual-semantic relationship. In this paper, we propose a geometry sensitive cross-modal reasoning network for CQBIR by jointly modeling the geometric information of the image and the visual-semantic relationship between the reference image and modification text in the query. Specifically, it contains two key components a geometry sensitive inter-modal attention module (GS-IMA) and a text-guided visual reasoning module (TG-VR). The GS-IMA introduces the spatial structure into the inter-modal attention in both implicit and explicit manners. The TG-VR models the unequal semantics not included in the reference image to guide further visual reasoning. As a result, our method can learn effective feature for the composed query which does not exhibit literal alignment. Comprehensive experimental results on three standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art methods.Conventional video compression (VC) methods are based on motion compensated transform coding, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized individually due to the combinatorial nature of the end-to-end optimization problem. Learned VC allows end-to-end rate-distortion (R-D) optimized training of nonlinear transform, motion and entropy model simultaneously. Most works on learned VC consider end-to-end optimization of a sequential video codec based on R-D loss averaged over pairs of successive frames. It is well-known in conventional VC that hierarchical, bi-directional coding outperforms sequential compression because of its ability to use both past and future reference frames. This paper proposes a learned hierarchical bi-directional video codec (LHBDC) that combines the benefits of hierarchical motion-compensated prediction and end-to-end optimization. Experimental results show that we achieve the best R-D results that are reported for learned VC schemes to date in both PSNR and MS-SSIM. Compared to conventional video codecs, the R-D performance of our end-to-end optimized codec outperforms those of both x265 and SVT-HEVC encoders ("veryslow" preset) in PSNR and MS-SSIM as well as HM 16.23 reference software in MS-SSIM. We present ablation studies showing performance gains due to proposed novel tools such as learned masking, flow-field subsampling, and temporal flow vector prediction. The models and instructions to reproduce our results can be found in https//github.com/makinyilmaz/LHBDC/.Coronary artery disease (CAD) is a leading cause of death globally. Computed tomography coronary angiography (CTCA) is a noninvasive imaging procedure for diagnosis of CAD. However, CTCA requires cardiac gating to ensure that diagnostic-quality images are acquired in all patients. Gating reliability could be improved by utilizing ultrasound (US) to provide a direct measurement of cardiac motion; however, commercially available US transducers are not computed tomography (CT) compatible. To address this challenge, a CT-compatible 2.5-MHz cardiac phased array transducer is developed via modeling, and then, an initial prototype is fabricated and evaluated for acoustic and radiographic performance. This 92-element piezoelectric array transducer is designed with a thin acoustic backing (6.5 mm) to reduce the volume of the radiopaque acoustic backing that typically causes arrays to be incompatible with CT imaging. This thin acoustic backing contains two rows of air-filled, triangular prism-shaped voids that operate as an acoustic diode. The developed transducer has a bandwidth of 50% and a single-element SNR of 9.9 dB compared to 46% and 14.7 dB for a reference array without an acoustic diode. In addition, the acoustic diode reduces the time-averaged reflected acoustic intensity from the back wall of the acoustic backing by 69% compared to an acoustic backing of the same composition and thickness without the acoustic diode. The feasibility of real-time echocardiography using this array is demonstrated in vivo, including the ability to image the position of the interventricular septum, which has been demonstrated to effectively predict cardiac motion for prospective, low radiation CTCA gating.Prostate segmentation in transrectal ultrasound (TRUS) image is an essential prerequisite for many prostate-related clinical procedures, which, however, is also a long-standing problem due to the challenges caused by the low image quality and shadow artifacts. In this paper, we propose a Shadow-consistent Semi-supervised Learning (SCO-SSL) method with two novel mechanisms, namely shadow augmentation (Shadow-AUG) and shadow dropout (Shadow-DROP), to tackle this challenging problem. Specifically, Shadow-AUG enriches training samples by adding simulated shadow artifacts to the images to make the network robust to the shadow patterns. Shadow-DROP enforces the segmentation network to infer the prostate boundary using the neighboring shadow-free pixels. Extensive experiments are conducted on two large clinical datasets (a public dataset containing 1,761 TRUS volumes and an in-house dataset containing 662 TRUS volumes). In the fully-supervised setting, a vanilla U-Net equipped with our Shadow-AUG&Shadow-DROP outperforms the state-of-the-arts with statistical significance.
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