Cardiac Outcome of Children With SARS-CoV-2 Related Multisystem Inflammatory Syndrome.

OBJECTIVE: To study the cardiac outcomes of patients with multisystem inflammatory syndrome in children (MIS-C) after 6-month of diagnosis. METHODS: This review of hospital records was conducted on MIS-C patients (aged <21 year) who completed a six-month follow up. The baseline demographic, clinical, laboratory, and treatment characteristics during the acute phase, and echocardiographic findings during follow-up were collected. RESULTS: 116 patients (61.2% male, median age 7 years) with MIS-C were included in the study. At the time of admission, cardiac abnormalities were present in 70.7% of MIS-C patients, and the most common cardiac abnormalities were valve failure (50.9%), followed by ventricular dysfunction (39.7%), and pericardial effusion (23.3%). Six month after diagnosis, cardiac abnormalities were found in 10.3% of patients, and patients had lower rates of ventricular dysfunction (P<0.001), valve failure (P<0.001), pericardial effusion (P<0.001), and coronary involvement (P<0.001) as composed to the baseline. Intravenous immunoglobulin (IVIG) and steroid treatment significantly reduced the odds of occurrence of ventricular dysfunction (P=0.002), valve failure (P=0.004), and low ejection fraction (P=0.002) in comparison to IVIG treatment. CONCLUSION: While most MIS-C patients had abnormal echocardiographic findings at admission, only 10.3% of patients had cardiac abnormalities during follow up.

Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides.

Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ultimate solution, one might even train a deep network from scratch with the domain-relevant images, a highly desirable option which is generally impeded in pathology by lack of labeled images and the computational expense. In this study, we propose a new network, namely KimiaNet, that employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations. We used more than 240,000 image patches with 1000×1000 pixels acquired at 20× magnification through our proposed "high-cellularity mosaic" approach to enable the usage of weak labels of 7126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through The Cancer Genome Atlas (TCGA) repository. We tested KimiaNet using three public datasets, namely TCGA, endometrial cancer images, and colorectal cancer images by evaluating the performance of search and classification when corresponding features of different networks are used for image representation. As well, we designed and trained multiple convolutional batch-normalized ReLU (CBR) networks. The results show that KimiaNet provides superior results compared to the original DenseNet and smaller CBR networks when used as feature extractor to represent histopathology images.

Energy and exergy analysis of a novel turbo-compounding system for supercharging and mild hybridization of a gasoline engine.

Number of hybrid vehicles has increased around the world significantly. Automotive industry is utilizing the hybridization of the powertrain system to achieve better fuel economic and emissions reduction. One of the options recently considered in research for hybridization and downsizing of vehicles is to employ waste heat recovery systems. In this paper, the addition of a turbo-compound system with an air Brayton cycle (ABC) to a naturally aspirated engine was studied in AVL BOOST software. In addition, a supercharger was modeled to charge extra air into the engine and ABC. The engine was first validated against the experimental data prior to turbo-compounding. The energy and exergy analysis was performed to understand the effects of the proposed design at engine rated speed. Results showed that between 16 and 18% increase in engine mechanical power can be achieved by adding turbo-compressor. Furthermore, the recommended ABC system can recover up to 1.1 kW extra electrical power from the engine exhaust energy. The energy and exergy efficiencies were both improved slightly by turbo-compounding and BSFC reduced by nearly 1% with the proposed system. Furthermore, installing the proposed system resulted in increase in backpressure up to approximately 23.8 kPa.

Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry.

We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data. We use neural networks to capture precursor and fragment ions across m/z, retention-time, and intensity dimensions. They are then further integrated with peptide sequence patterns to address the problem of highly multiplexed spectra. DIA coupled with de novo sequencing allowed us to identify novel peptides in human antibodies and antigens.

Fast and Scalable Feature Selection for Gene Expression Data Using Hilbert-Schmidt Independence Criterion.

GOAL: In computational biology, selecting a small subset of informative genes from microarray data continues to be a challenge due to the presence of thousands of genes. This paper aims at quantifying the dependence between gene expression data and the response variables and to identifying a subset of the most informative genes using a fast and scalable multivariate algorithm. METHODS: A novel algorithm for feature selection from gene expression data was developed. The algorithm was based on the Hilbert-Schmidt independence criterion (HSIC), and was partly motivated by singular value decomposition (SVD). RESULTS: The algorithm is computationally fast and scalable to large datasets. Moreover, it can be applied to problems with any type of response variables including, biclass, multiclass, and continuous response variables. The performance of the proposed algorithm in terms of accuracy, stability of the selected genes, speed, and scalability was evaluated using both synthetic and real-world datasets. The simulation results demonstrated that the proposed algorithm effectively and efficiently extracted stable genes with high predictive capability, in particular for datasets with multiclass response variables. CONCLUSION/SIGNIFICANCE: The proposed method does not require the whole microarray dataset to be stored in memory, and thus can easily be scaled to large datasets. This capability is an important attribute in big data analytics, where data can be large and massively distributed.

Designing the inner surface corrugations of hollow fibers to enhance CO2 absorption efficiency.

For the first time, a low cost strategy is introduced to enhance the efficiency of CO2 absorption using gas-liquid membrane contactors. This is implemented by designing the corrugations in the inner layer of poly(vinyl chloride) hollow fibers (PVC HFs) through changing the bore fluid composition. In fact, the number of corrugations in the HF inner layer is engineered via changing the phase separation time within the inner layer. Such that expedited phase separation leads to highly corrugated inner layer. In contrast, decelerated phase separation is responsible for reduced number of inner layer corrugations. Phase separation causes the initial polymer solution with low viscoelastic moduli to be transferred into polymer-rich domains with high viscoelastic moduli. These domains resist against stretching-induced radial forces toward the center of HF; therefore, the inner layer of HF buckles. Delayed phase separation defers formation of polymer-rich domains and hence, HF with less corrugated inner surface is expected. The phase separation within the HF inner layer is controlled through changing the rate of solvent/nonsolvent exchange. This is conducted by variation the solvent content in the bore fluid; as higher as solvent content, as slower as solvent/nonsolvent exchange.

Determining protein structures from NOESY distance constraints by semidefinite programming.

Contemporary practical methods for protein nuclear magnetic resonance (NMR) structure determination use molecular dynamics coupled with a simulated annealing schedule. The objective of these methods is to minimize the error of deviating from the nuclear overhauser effect (NOE) distance constraints. However, the corresponding objective function is highly nonconvex and, consequently, difficult to optimize. Euclidean distance matrix (EDM) methods based on semidefinite programming (SDP) provide a natural framework for these problems. However, the high complexity of SDP solvers and the often noisy distance constraints provide major challenges to this approach. The main contribution of this article is a new SDP formulation for the EDM approach that overcomes these two difficulties. We model the protein as a set of intersecting two- and three-dimensional cliques. Then, we adapt and extend a technique called semidefinite facial reduction to reduce the SDP problem size to approximately one quarter of the size of the original problem. The reduced SDP problem can be solved approximately 100 times faster, and it is also more resistant to numerical problems from erroneous and inexact distance bounds.

The use of natural biopolymer of chitosan as biodegradable beads for local antibiotic delivery: release studies.

BACKGROUND: Chitosan is a naturally occurring biopolymer which has been widely used in a variety of biomedical applications including local antibiotic delivery due to its excellent mechanical properties, biodegradability and biocompatibility. Beads are spherical, porous carriers which are prepared from various materials including chitosan. OBJECTIVES: The current study aimed to fabricate a new controlled delivery system for local anti-infective treatment and to study its release behavior. MATERIALS AND METHODS: Twenty beads were prepared from 1% or 2% chitosan solutions and immersed in vancomycin (VM) or teicoplanin (TN) solutions. The antibiotic release kinetics was determined by linear regression analysis supposing first order kinetics. RESULTS: Immersion for 3 h resulted in significant increase in the total TN release that differed from 0.5 h of immersion, except for the 1% beads immersed in VM. Increasing the chitosan concentration significantly increased the total release and antibiotic load of beads. The release of TN was more delayed compared to that of VM, which allowed a gradual release beyond 3 days. The half-life (mean ± SEM) of both types of TN-containing beads was significantly extended for 3 h immersion in comparison to 0.5 h immersion (26.1 ± 5.9 vs 10.9 ± 1.0 and 17.0 ± 2.1 vs 5.1 ± 1.9; P < 0.001). However, neither increasing the chitosan concentration, nor immersion time did result in any significant increase in the release of VM. CONCLUSIONS: The current study demonstrated an improved control of TN release impregnated in beads. It can be concluded that chitosan beads might be considered as a novel carrier for TN delivery to infected bone for local anti-infective therapy.

Automatic basis selection techniques for RBF networks.

This paper proposes a generic criterion that defines the optimum number of basis functions for radial basis function (RBF) neural networks. The generalization performance of an RBF network relates to its prediction capability on independent test data. This performance gives a measure of the quality of the chosen model. An RBF network with an overly restricted basis gives poor predictions on new data, since the model has too little flexibility (yielding high bias and low variance). By contrast, an RBF network with too many basis functions also gives poor generalization performance since it is too flexible and fits too much of the noise on the training data (yielding low bias but high variance). Bias and variance are complementary quantities, and it is necessary to assign the number of basis function optimally in order to achieve the best compromise between them. In this paper we use Stein's unbiased risk estimator to derive an analytical criterion for assigning the appropriate number of basis functions. Two cases of known and unknown noise have been considered and the efficacy of this criterion in both situations is illustrated experimentally. The paper also shows an empirical comparison between this method and two well known classical methods, cross validation and the Bayesian information criterion, BIC.