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Median peak concentration in the RCJ was 1331.4 μg/mL with technique P and 683.1 μg/mL with technique PD. Median Tmax occurred at 30 minutes with technique P and 25 minutes with technique PD. No significant (Cmax, P = 0.18; Tmax, P = 0.6) difference in amikacin Cmax or Tmax between techniques was detected.

Placement of 2 WRTs offers no advantage to a single proximal WRT when performing IVRLP to deliver maximal amikacin concentrations to the RCJ using IVRLP.

Placement of 2 WRTs offers no advantage to a single proximal WRT when performing IVRLP to deliver maximal amikacin concentrations to the RCJ using IVRLP.

To compare the inflammatory response of murine macrophages exposed to the enteric microbiome of obese horses versus nonobese horses.

Fecal samples from 12 obese horses (body condition score ≥ 7/9) and 12 nonobese horses (body condition score 4 to 5/9) with similar dietary management.

Fecal supernatant was prepared from frozen fecal samples. RAW 264.7 macrophage cells were exposed to the fecal extract. Inflammatory cytokine (interleukin-1β, tumor necrosis factor-α, and interleukin-6) gene expression was quantified via real-time quantitative reverse transcription PCR assay, and cytokine concentration was quantified via ELISA. Lipopolysaccharide was evaluated in fecal extract via chromo-limulus amoebocyte lysate assay.

Compared with fecal extracts from nonobese horses, fecal extracts from obese horses presented higher concentrations of lipopolysaccharide and induced a heightened expression of the proinflammatory cytokines interleukin-1β, tumor necrosis factor-α, and interleukin-6 from macrophages.

The e horse have not been fully elucidated. Improved understanding of the pathophysiology of disease will guide future research into potential diagnostic and therapeutic interventions for equine obesity.

To compare the serum cobalamin concentrations in canine parvovirus (CPV)-infected dogs with those of healthy control dogs.

45 dogs with CPV enteritis and 17 healthy age-matched control dogs.

Infection was confirmed by visualization of CPV-2 through fecal electron microscopy. All dogs received supportive care. Serum samples taken at admission were used to determine cobalamin, C-reactive protein, and albumin concentrations.

Serum cobalamin concentrations were significantly lower in the CPV-infected group (median [interquartile range], 173 pmol/L [< 111 to 722 pmol/L]) than in healthy control dogs (379 pmol/L [193 to > 738 pmol/L). There was no association between cobalamin concentration and C-reactive protein or albumin concentration.

While hypocobalaminemia was common in CPV-infected dogs, the clinical relevance of this finding remains to be determined. Studies assessing markers of cellular cobalamin deficiency in dogs with CPV infection appear warranted.

While hypocobalaminemia was common in CPV-infected dogs, the clinical relevance of this finding remains to be determined. Studies assessing markers of cellular cobalamin deficiency in dogs with CPV infection appear warranted.The COVID-19 pandemic has overwhelmed health care systems worldwide, particularly in underresourced communities of color with a high prevalence of pre-existing health conditions. Many state governments and health care entities responded by increasing their capacity for telemedicine and disease tracking and creating mobile apps for dissemination of medical information. Our experiences with state-sponsored apps suggest that because many of these eHealth tools did not include community participation, they inadvertently contributed to widening digital health disparities. We propose that, as eHealth tools continue to expand as a form of health care, more attention needs to be given to their equitable distribution, accessibility, and usage. In this viewpoint collaboratively written by a minority-serving community-based organization and an eHealth academic research team, we present our experience participating in a community advisory board working on the dissemination of the COVID Alert NY mobile app to illustrate the importance of public participation in app development. We also provide practical recommendations on how to involve community representatives in the app development process. We propose that transparency and community involvement in the process of app development ultimately increases buy-in, trust, and usage of digital technology in communities where they are needed most.Blood pressure (BP) is one of the most important indicators of health. BP that is too high or too low causes varying degrees of diseases, such as renal impairment, cerebrovascular incidents, and cardiovascular diseases. Since traditional cuff-based BP measurement techniques have the drawbacks of patient discomfort and the impossibility of continuous BP monitoring, noninvasive cuffless continuous BP measurement has become a popular topic. The common noninvasive approach uses machine-learning (ML) algorithms to estimate BP by using the features extracted from simultaneous photoplethysmogram (PPG) and electrocardiogram (ECG) signals, such as the pulse transit time and pulse wave velocity. This study investigates the BP estimation performance of the novel dendritic neural regression (DNR) method proposed by us. Unlike conventional neural networks, DNR utilizes the multiplication operator as the excitation function in each dendritic branch, inspired by biological neuron phenomena, and can effectively capture nonlinear relationships between distinct input features. In addition, AMSGrad is used as the optimization algorithm to further enhance the dendritic neural model's performance. The experimental results show that by being fed a combination of the raw features extracted from the ECG and PPG signals and the components of the BP mathematical models, DNR can increase the accuracy of systolic BP, diastolic BP, and mean arterial pressure estimation significantly, which are superior to the state-of-the-art ML techniques. According to the British Hypertension Society protocol, DNR achieves a grade of A for the long-term BP estimation. Considering its architectural simplicity and powerful performance, the proposed method can be regarded as a reliable tool for estimating long-term continuous BP in a noninvasive cuffless way.As a novel neural network-based learning framework, a broad learning system (BLS) has attracted much attention due to its excellent performance on regression and balanced classification problems. However, it is found to be unsuitable for imbalanced data classification problems because it treats each class in an imbalanced dataset equally. To address this issue, this work proposes a weighted BLS (WBLS) in which the weight assigned to each class depends on the number of samples in it. In order to further boost its classification performance, an improved differential evolution algorithm is proposed to automatically optimize its parameters, including the ones in BLS and newly generated weights. We first optimize the parameters with a training dataset, and then apply them to WBLS on a test dataset. The experiments on 20 imbalanced classification problems have shown that our proposed method can achieve higher classification accuracy than the other methods in terms of several widely used performance metrics. Finally, it is applied to fault diagnosis in self-organizing cellular networks to further show its applicability to industrial application problems.The existing deep multiview clustering (MVC) methods are mainly based on autoencoder networks, which seek common latent variables to reconstruct the original input of each view individually. However, due to the view-specific reconstruction loss, it is challenging to extract consistent latent representations over multiple views for clustering. To address this challenge, we propose adversarial MVC (AMvC) networks in this article. The proposed AMvC generates each view's samples conditioning on the fused latent representations among different views to encourage a more consistent clustering structure. Specifically, multiview encoders are used to extract latent descriptions from all the views, and the corresponding generators are used to generate the reconstructed samples. The discriminative networks and the mean squared loss are jointly utilized for training the multiview encoders and generators to balance the distinctness and consistency of each view's latent representation. Moreover, an adaptive fusion layer is developed to obtain a shared latent representation, on which a clustering loss and the l1,2 -norm constraint are further imposed to improve clustering performance and distinguish the latent space. Experimental results on video, image, and text datasets demonstrate that the effectiveness of our AMvC is over several state-of-the-art deep MVC methods.Considering that cooperative interactions and antagonistic interactions between neighboring agents may exist simultaneously in practice, this article studies the bipartite time-varying output formation tracking (BTVOFT) problems for homogeneous/heterogeneous multiagent systems with multiple nonautonomous leaders under switching communication networks. First, a full-dimensional observer-based nonsmooth distributed dynamic event-triggered (DDET) output feedback control scheme is proposed to ensure that BTVOFT is achieved, and the Zeno behavior is excluded. Note that the nonsmooth distributed control scheme requires global communication network information and may cause unexpected chattering effect, and the design cost of full-dimensional observer is relatively high. Thus, a reduced-dimensional observer-based continuous fully DDET scheme is proposed. Compared with the existing event-triggered schemes, the dynamic event-triggered scheme can ensure larger interevent times by introducing an additional internal dynamic variable. Finally, the effectiveness and performance of the theoretical results are validated by numerical simulations.In this work, we describe our efforts in addressing two typical challenges involved in the popular text classification methods when they are applied to text moderation the representation of multibyte characters and word obfuscations. Specifically, a multihot byte-level scheme is developed to significantly reduce the dimension of one-hot character-level encoding caused by the multiplicity of instance-scarce non-ASCII characters. In addition, we introduce a simple yet effective weighting approach for fusing n-gram features to empower the classical logistic regression. Surprisingly, it outperforms well-tuned representative neural networks greatly. As a continual effort toward text moderation, we endeavor to analyze the current state-of-the-art (SOTA) algorithm bidirectional encoder representations from transformers (BERT), which works well in context understanding but performs poorly on intentional word obfuscations. To resolve this crux, we then develop an enhanced variant and remedy this drawback by integrating byte and character decomposition. It advances the SOTA performance on the largest abusive language datasets as demonstrated by our comprehensive experiments. Our work offers a feasible and effective framework to tackle word obfuscations.Semantic segmentation has been widely investigated in the community, in which state-of-the-art techniques are based on supervised models. Those models have reported unprecedented performance at the cost of requiring a large set of high quality segmentation masks for training. Obtaining such annotations is highly expensive and time consuming, in particular, in semantic segmentation where pixel-level annotations are required. In this work, we address this problem by proposing a holistic solution framed as a self-training framework for semi-supervised semantic segmentation. The key idea of our technique is the extraction of the pseudo-mask information on unlabelled data whilst enforcing segmentation consistency in a multi-task fashion. We achieve this through a three-stage solution. Firstly, a segmentation network is trained using the labelled data only and rough pseudo-masks are generated for all images. this website Secondly, we decrease the uncertainty of the pseudo-mask by using a multi-task model that enforces consistency and that exploits the rich statistical information of the data.