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The effect of EO on enzymatic activity of T. castaneum adults was examined using Acetylcholinesterase, α-Carboxylesterase, β-Carboxylesterase, Glutathione-S-Transferase, Acid and Alkaline phosphatase assays. The results indicated that the activity of detoxification enzymes drastically varied when compared with control. This EO had toxicant effects on all stages of the life of T. castaneum.
Infections with Ascaris lumbricoides and Trichuris trichiura remain significant contributors to the global burden of neglected tropical diseases. Infection may in particular affect child development as they are more likely to be infected with T. trichiura and/or A. lumbricoides and to carry higher worm burdens than adults. Whilst the impact of heavy infections are clear, the effects of moderate infection intensities on the growth and development of children remain elusive. Field studies are confounded by a lack of knowledge of infection history, nutritional status, presence of co-infections and levels of exposure to infective eggs. Therefore, animal models are required. Given the physiological similarities between humans and pigs but also between the helminths that infect them; A. suum and T. suis, growing pigs provide an excellent model to investigate the direct effects of Ascaris spp. and Trichuris spp. on weight gain.

We employed a trickle infection protocol to mimic natural co-infection to assess the igs but also that T. suis infection may be more detrimental that A. suum on growth.The cognitive impairment, depression, a decrease in the ability to perform activities of daily living (ADLs), and salivary gland dysfunction, as indicated by the reduction of alpha-amylase activity, have been reported in patients with type 2 diabetes (T2DM). However, the effects of depression on cognitive function, salivary alpha-amylase activity, and ADLs in T2DM patients have never been investigated. In this study, 115 participants were divided into three groups, including 30 healthy people, 50 T2DM patients without depression, and 35 T2DM patients with depression. Then, the cognitive function, the level of depression, salivary-alpha amylase activity, ADLs, and metabolic parameters were determined. Results showed that T2DM patients had hyperglycemia and cognitive impairment. A decrease in the salivary alpha-amylase activity was observed in T2DM patients. Interestingly, T2DM patients with depression had higher level of hyperglycemia and cognitive impairment than T2DM patients. Additionally, cognitive function was associated with the salivary-alpha amylase activity in T2DM without depression, while the severity of depression was associated with the salivary-alpha amylase activity in T2DM patients with depression. Therefore, we concluded that T2DM caused the impairment of metabolism, decreased salivary alpha-amylase activity, and cognitive impairment. Furthermore, T2DM patients with depression had higher level of hyperglycemia and cognitive decline than T2DM patients.
Histotripsy is an emerging noninvasive, nonionizing and nonthermal focal cancer therapy that is highly precise and can create a treatment zone of virtually any size and shape. Current histotripsy systems rely on ultrasound imaging to target lesions. However, deep or isoechoic targets obstructed by bowel gas or bone can often not be treated safely using ultrasound imaging alone. This work presents an alternative x-ray C-arm based targeting approach and a fully automated robotic targeting system.

The approach uses conventional cone beam CT (CBCT) images to localize the target lesion and 2D fluoroscopy to determine the 3D position and orientation of the histotripsy transducer relative to the C-arm. The proposed pose estimation uses a digital model and deep learning-based feature segmentation to estimate the transducer focal point relative to the CBCT coordinate system. Additionally, the integrated robotic arm was calibrated to the C-arm by estimating the transducer pose for four preprogrammed transducer orientations and positions. The calibrated system can then automatically position the transducer such that the focal point aligns with any target selected in a CBCT image.

The accuracy of the proposed targeting approach was evaluated in phantom studies, where the selected target location was compared to the center of the spherical ablation zones in post-treatment CBCTs. The mean and standard deviation of the Euclidean distance was 14 _ 05 mm. The mean absolute error of the predicted treatment radius was 05 _ 05 mm.

CBCT-based histotripsy targeting enables accurate and fully automated treatment without ultrasound guidance.

The proposed approach could considerably decrease operator dependency and enable treatment of tumors not visible under ultrasound.
The proposed approach could considerably decrease operator dependency and enable treatment of tumors not visible under ultrasound.Clinically, retinal vessel segmentation is a significant step in the diagnosis of fundus diseases. However, recent methods generally neglect the difference of semantic information between deep and shallow features, which fail to capture the global and local characterizations in fundus images simultaneously, resulting in the limited segmentation performance for fine vessels. In this article, a global transformer (GT) and dual local attention (DLA) network via deep-shallow hierarchical feature fusion (GT-DLA-dsHFF) are investigated to solve the above limitations. First, the GT is developed to integrate the global information in the retinal image, which effectively captures the long-distance dependence between pixels, alleviating the discontinuity of blood vessels in the segmentation results. Second, DLA, which is constructed using dilated convolutions with varied dilation rates, unsupervised edge detection, and squeeze-excitation block, is proposed to extract local vessel information, consolidating the edge details in the segmentation result. Finally, a novel deep-shallow hierarchical feature fusion (dsHFF) algorithm is studied to fuse the features in different scales in the deep learning framework, respectively, which can mitigate the attenuation of valid information in the process of feature fusion. selleck chemical We verified the GT-DLA-dsHFF on four typical fundus image datasets. The experimental results demonstrate our GT-DLA-dsHFF achieves superior performance against the current methods and detailed discussions verify the efficacy of the proposed three modules. Segmentation results of diseased images show the robustness of our proposed GT-DLA-dsHFF. Implementation codes will be available on https//github.com/YangLibuaa/GT-DLA-dsHFF.This article explores aggregative games in a network of general linear systems subject to external disturbances. To deal with external disturbances, distributed strategy-updating rules based on the internal model are proposed for the case with perfect and imperfect information, respectively. Different from the existing algorithms based on gradient dynamics, by introducing the integral of the gradient of cost functions on the basis of the passivity theory, the rules are proposed to force the strategies of all agents to evolve to the Nash equilibrium, regardless of the effect of disturbances. The convergence of the two strategy-updating rules is analyzed via the Lyapunov stability theory, passivity theory, and singular perturbation theory. Simulations are performed to illustrate the effectiveness of the proposed methods.In real industries, there often exist application scenarios where the target domain holds fault categories never observed in the source domain, which is an open-set domain adaptation (DA) diagnosis issue. Existing DA diagnosis methods under the assumption of sharing identical label space across domains fail to work. What is more, labeled samples can be collected from different sources, where multisource information fusion is rarely considered. To handle this issue, a multisource open-set DA diagnosis approach is developed. Specifically, multisource domain data of different operation conditions sharing partial classes are adopted to take advantage of fault information. Then, an open-set DA network is constructed to mitigate the domain gap across domains. Finally, a weighting learning strategy is introduced to adaptively weigh the importance on feature distribution alignment between known class and unknown class samples. Extensive experiments suggest that the proposed approach can substantially boost the performance of open-set diagnosis issues and outperform existing diagnosis approaches.Glass is very common in our daily life. Existing computer vision systems neglect it and thus may have severe consequences, e.g., a robot may crash into a glass wall. However, sensing the presence of glass is not straightforward. The key challenge is that arbitrary objects/scenes can appear behind the glass. In this paper, we propose an important problem of detecting glass surfaces from a single RGB image. To address this problem, we construct the first large-scale glass detection dataset (GDD) and propose a novel glass detection network, called GDNet-B, which explores abundant contextual cues in a large field-of-view via a novel large-field contextual feature integration (LCFI) module and integrates both high-level and low-level boundary features with a boundary feature enhancement (BFE) module. Extensive experiments demonstrate that our GDNet-B achieves satisfying glass detection results on the images within and beyond the GDD testing set. We further validate the effectiveness and generalization capability of our proposed GDNet-B by applying it to other vision tasks, including mirror segmentation and salient object detection. Finally, we show the potential applications of glass detection and discuss possible future research directions.In this paper, we present a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typically, affine or quadratic motion models. The core idea of our work is to leverage the Expectation-Maximization (EM) framework in order to design in a well-founded manner a loss function and a training procedure of our motion segmentation neural network that does not require either ground-truth or manual annotation. However, in contrast to the classical iterative EM, once the network is trained, we can provide a segmentation for any unseen optical flow field in a single inference step and without estimating any motion models. We investigate different loss functions including robust ones and propose a novel efficient data augmentation technique on the optical flow field, applicable to any network taking optical flow as input. In addition, our method is able by design to segment multiple motions. Our motion segmentation network was tested on four benchmarks, DAVIS2016, SegTrackV2, FBMS59, and MoCA, and performed very well, while being fast at test time.Real world data often exhibits a long-tailed and open-ended (i.e., with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes). We define Open Long-Tailed Recognition++ (OLTR++) as learning from such naturally distributed data and optimizing for the classification accuracy over a balanced test set which includes both known and open classes. OLTR++ handles imbalanced classification, few-shot learning, open-set recognition, and active learning in one integrated algorithm, whereas existing classification approaches often focus only on one or two aspects and deliver poorly over the entire spectrum. The key challenges are 1) how to share visual knowledge between head and tail classes, 2) how to reduce confusion between tail and open classes, and 3) how to actively explore open classes with learned knowledge. Our algorithm, OLTR++, maps images to a feature space such that visual concepts can relate to each other through a memory association mechanism and a learned metric (dynamic meta-embedding) that both respects the closed world classification of seen classes and acknowledges the novelty of open classes.
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