Dedicator of cytokinesis 8 (DOCK8) mutation impairs the differentiation of helper T cells by regulating the glycolytic pathway of CD4+ T cells.

Dedicator of cytokinesis 8 (DOCK8) deficiency is a primary immunodeficiency disease caused by mutations in exon 45 of the DOCK8 gene. The clinical signs primarily consist of increased serum IgE levels, eczema, repeated skin infections, allergies, and upper respiratory tract infections. Using CRISPR/Cas9 technology, we generated a DOCK8 exon 45 mutation in mice, mirroring the mutation found in patients. The results indicated that DOCK8 mutation impairs peripheral T cell homeostasis, disrupts regulatory T cells (Tregs) development, increases ICOS expression in Tregs within peripheral lymph nodes (pLn), and promotes Th17 cell differentiation within the spleen and pLn. Upon virus infection, DOCK8 mutation CD4+ T cells have a Th2 effector fate. RNA-bulk sequencing data revealed alternations in the mTOR pathway of DOCK8 mutant CD4+ T cells. We observed that DOCK8 mutation upregulates the glycolysis levels in CD4+ T cells, which is related to the Akt/mTOR/S6/HIF-1α pathway. In summary, our research elucidates that DOCK8 regulates the differentiation of helper T cells by modulating the glycolytic pathway in CD4+ T cells, thereby advancing the comprehension and offering potential treatment of diseases in DOCK8-deficient patients.

Adaptive Multimode Process Monitoring Based on Mode-Matching and Similarity-Preserving Dictionary Learning.

In real industrial processes, factors, such as the change in manufacturing strategy and production technology lead to the creation of multimode industrial processes and the continuous emergence of new modes. Although the industrial SCADA system has accumulated a large amount of historical data, which can be used for modeling and monitoring multimode processes to a certain extent, it is difficult for the model learned from historical data to adapt to emerging modes, resulting in the model mismatch. On the other hand, updating the model with data from new modes allows the model to continuously match the new modes, but it may cause the model to lose the ability to represent the historical modes, resulting in "catastrophic forgetting." To address these problems, this article proposed a jointly mode-matching and similarity-preserving dictionary learning (JMSDL) method, which updated the model by learning the data of new modes, so that the model can adaptively match the newly emerged modes. At the same time, a similarity metric was put forward to guarantee the representation ability of the proposed method for historical data. A numerical simulation experiment, the CSTH process experiment, and an industrial roasting process experiment indicated that the proposed JMSDL method can match new modes while maintaining its performance on the historical modes accurately. In addition, the proposed method significantly outperforms the state-of-the-art methods in terms of fault detection and false alarm rate.

CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence.

In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especially in the area of digital healthcare. In digital healthcare, medical edge devices empowered by CPU-GPU (Graphics Processing Unit) cooperative multiprocessor system-on-chips (MPSoCs) have a great potential in processing and managing the massive amounts of health-related data. However, most of the existing works on CPU-GPU cooperative MPSoCs cannot maintain a high-precision workload estimation since they simply leverage the worst-case execution cycles to pessimistically predict the workload of digital healthcare applications. Besides, they neglect the personalized requirements of individual healthcare applications and the lifetime reliability demands of heterogeneous CPU-GPU cores. As a result, the normal functions of medical edge devices and the quality-of-services (QoS) of digital healthcare applications are likely to suffer from underlying failures and degradation. In this paper, we explore CPU-GPU cooperative QoS optimization of personalized digital healthcare applications running on reliability guaranteed edge devices with the help of machine learning and swarm intelligence techniques. We first develop two novel predictors: one is a machine learning based predictor for application workload estimation, and the other is a feature-driven predictor for application QoS estimation. We then incorporate the two predictors into a swarm intelligent application scheduling scheme upon the cooperative dual-population evolutionary algorithm (c-DPEA) to find optimal application mapping and partitioning settings. Experimental results show that our solution not only augments the average QoS of whole digital healthcare applications by 15.7%, but also balances the QoS of individual digital healthcare applications by 64.3%.

A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky⁻Golay Filter for ECG Denoising.

Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.

Physical-level synthesis for digital lab-on-a-chip considering variation, contamination, and defect.

Microfluidic lab-on-a-chips have been widely utilized in biochemical analysis and human health studies due to high detection accuracy, high timing efficiency, and low cost. The increasing design complexity of lab-on-a-chips necessitates the computer-aided design (CAD) methodology in contrast to the classical manual design methodology. A key part in lab-on-a-chip CAD is physical-level synthesis. It includes the lab-on-a-chip placement and routing, where placement is to determine the physical location and the starting time of each operation and routing is to transport each droplet from the source to the destination. In the lab-on-a-chip design, variation, contamination, and defect need to be considered. This work designs a physical-level synthesis flow which simultaneously considers variation, contamination, and defect of the lab-on-a-chip design. It proposes a maze routing based, variation, contamination, and defect aware droplet routing technique, which is seamlessly integrated into an existing placement technique. The proposed technique improves the placement solution for routing and achieves the placement and routing co-optimization to handle variation, contamination, and defect. The simulation results demonstrate that our technique does not use any defective/contaminated grids, while the technique without considering contamination and defect uses 17.0% of the defective/contaminated grids on average. In addition, our routing variation aware technique significantly improves the average routing yield by 51.2% with only 3.5% increase in completion time compared to a routing variation unaware technique.

Nonlocal regularization for active appearance model: Application to medial temporal lobe segmentation.

The human medial temporal lobe (MTL) is an important part of the limbic system, and its substructures play key roles in learning, memory, and neurodegeneration. The MTL includes the hippocampus (HC), amygdala (AG), parahippocampal cortex (PHC), entorhinal cortex, and perirhinal cortex--structures that are complex in shape and have low between-structure intensity contrast, making them difficult to segment manually in magnetic resonance images. This article presents a new segmentation method that combines active appearance modeling and patch-based local refinement to automatically segment specific substructures of the MTL including HC, AG, PHC, and entorhinal/perirhinal cortex from MRI data. Appearance modeling, relying on eigen-decomposition to analyze statistical variations in image intensity and shape information in study population, is used to capture global shape characteristics of each structure of interest with a generative model. Patch-based local refinement, using nonlocal means to compare the image local intensity properties, is applied to locally refine the segmentation results along the structure borders to improve structure delimitation. In this manner, nonlocal regularization and global shape constraints could allow more accurate segmentations of structures. Validation experiments against manually defined labels demonstrate that this new segmentation method is computationally efficient, robust, and accurate. In a leave-one-out validation on 54 normal young adults, the method yielded a mean Dice κ of 0.87 for the HC, 0.81 for the AG, 0.73 for the anterior parts of the parahippocampal gyrus (entorhinal and perirhinal cortex), and 0.73 for the posterior parahippocampal gyrus.

Volumetric analysis of medial temporal lobe structures in brain development from childhood to adolescence.

Puberty is an important stage of development as a child's sexual and physical characteristics mature because of hormonal changes. To better understand puberty-related effects on brain development, we investigated the magnetic resonance imaging (MRI) data of 306 subjects from 4 to 18 years of age. Subjects were grouped into before and during puberty groups according to their sexual maturity levels measured by the puberty scores. An appearance model-based automatic segmentation method with patch-based local refinement was employed to segment the MRI data and extract the volumes of medial temporal lobe (MTL) structures including the amygdala (AG), the hippocampus (HC), the entorhinal/perirhinal cortex (EPC), and the parahippocampal cortex (PHC). Our analysis showed age-related volumetric changes for the AG, HC, right EPC, and left PHC but only before puberty. After onset of puberty, these volumetric changes then correlate more with sexual maturity level, as measured by the puberty score. When normalized for brain volume, the volumes of the right HC decrease for boys; the volumes of the left HC increase for girls; and the volumes of the left and right PHC decrease for boys. These findings suggest that the rising levels of testosterone in boys and estrogen in girls might have opposite effects, especially for the HC and the PHC. Our findings on sex-specific and sexual maturity-related volumes may be useful in better understanding the MTL developmental differences and related learning, memory, and emotion differences between boys and girls during puberty.

Appearance-based modeling for segmentation of hippocampus and amygdala using multi-contrast MR imaging.

A new automatic model-based segmentation scheme that combines level set shape modeling and active appearance modeling (AAM) is presented. Since different MR image contrasts can yield complementary information, multi-contrast images can be incorporated into the active appearance modeling to improve segmentation performance. During active appearance modeling, the weighting of each contrast is optimized to account for the potentially varying contribution of each image while optimizing the model parameters that correspond to the shape and appearance eigen-images in order to minimize the difference between the multi-contrast test images and the ones synthesized from the shape and appearance modeling. As appearance-based modeling techniques are dependent on the initial alignment of training data, we compare (i) linear alignment of whole brain, (ii) linear alignment of a local volume of interest and (iii) non-linear alignment of a local volume of interest. The proposed segmentation scheme can be used to segment human hippocampi (HC) and amygdalae (AG), which have weak intensity contrast with their background in MRI. The experiments demonstrate that non-linear alignment of training data yields the best results and that multimodal segmentation using T1-weighted, T2-weighted and proton density-weighted images yields better segmentation results than any single contrast. In a four-fold cross validation with eighty young normal subjects, the method yields a mean Dice к of 0.87 with intraclass correlation coefficient (ICC) of 0.946 for HC and a mean Dice к of 0.81 with ICC of 0.924 for AG between manual and automatic labels.

Multiscale variation-aware techniques for high-performance digital microfluidic lab-on-a-chip component placement.

The invention of microfluidic lab-on-a-chip alleviates the burden of traditional biochemical laboratory procedures which are often very expensive. Device miniaturization and increasing design complexity have mandated a shift in digital microfluidic lab-on-a-chip design from traditional manual design to computer-aided design (CAD) methodologies. As an important procedure in the lab-on-a-chip layout CAD, the lab-on-a-chip component placement determines the physical location and the starting time of each operation such that the overall completion time is minimized while satisfying nonoverlapping constraint, resource constraint, and scheduling constraint. In this paper, a multiscale variation-aware optimization technique based on integer linear programming is proposed for the lab-on-a-chip component placement. The simulation results demonstrate that without considering variations, our technique always satisfies the design constraints and largely outperforms the state-of-the-art approach, with up to 65.9% reduction in completion time. When considering variations, the variation-unaware design has the average yield of 2%, while our variation-aware technique always satisfies the yield constraint with only 7.7% completion time increase.

Joint level-set shape modeling and appearance modeling for brain structure segmentation.

This paper presents a new fully automatic model-based segmentation algorithm, which combines level-set methods to model the shape of brain structures and their variation with active appearance modeling to generate images that are used to drive the segmentation. The new algorithm incorporates multi-modality images to improve the segmentation performance and the recursive least square (RLS) algorithm is adopted to minimize the difference between test image and the one synthesized from the shape and appearance modeling. When compared with manual segmentation, the 2D and 3D experiments demonstrate that the new algorithm is computationally efficient and robust and is promising for automatic segmentation of the lateral ventricles.

Optimal time points sampling in pathway modelling.

Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.