The purpose of this study was to assess the metabolic profile of plasma samples from cows with clinical and subclinical ketosis. According to clinical signs and 3-hydroxybutyrate plasma levels, 81 ...multiparous Holstein cows were selected from a dairy farm 7 to 21 d after calving. The cows were divided into 3 groups: cows with clinical ketosis, cows with subclinical ketosis, and healthy control cows. (1)H-Nuclear magnetic resonance-based metabolomics was used to assess the plasma metabolic profiles of the 3 groups. The data were analyzed by principal component analysis, partial least squares discriminant analysis, and orthogonal partial least-squares discriminant analysis. The differences in metabolites among the 3 groups were assessed. The orthogonal partial least-squares discriminant analysis model differentiated the 3 groups of plasma samples. The model predicted clinical ketosis with a sensitivity of 100% and a specificity of 100%. In the case of subclinical ketosis, the model had a sensitivity of 97.0% and specificity of 95.7%. Twenty-five metabolites, including acetoacetate, acetone, lactate, glucose, choline, glutamic acid, and glutamine, were different among the 3 groups. Among the 25 metabolites, 4 were upregulated, 7 were downregulated, and 14 were both upregulated and downregulated. The results indicated that plasma (1)H-nuclear magnetic resonance-based metabolomics, coupled with pattern recognition analytical methods, not only has the sensitivity and specificity to distinguish cows with clinical and subclinical ketosis from healthy controls, but also has the potential to be developed into a clinically useful diagnostic tool that could contribute to a further understanding of the disease mechanisms.
With the innovations in commercial-electronics products and fierce competition in the global panel industry, most panel manufacturers have adopted the small batch production mode to deal with a wide ...range of customization. With small batch sizes, productive yield and troubleshooting are considered the top priority, so monitoring capability conducted via virtual metrology (VM) is essentially necessary to satisfy the requirement of process quality. In this article, the real-world dataset involving the photoresist (PR) spacer heights of the color filter (CF) in the array sector of thin-film transistor liquid crystal display (TFT-LCD) manufacturing is investigated. In practice, the PR spacer heights can only be measured in an infrequent manner due to the scheduling restriction. Without taking additional sample measurements, how to design a high-accuracy VM model based on small batch sizes warrants urgent research for the TFT-LCD industry. The proposed framework can be divided into two parts. First, two novel distance-measuring methods are proposed, direct (fully connected) neural network (DNN) and Formula Omitted-means, to allocate the most affiliated positions so that the VM system can utilize these positions to execute metrology prediction on the same product. Next, a modified random forests (RFs) regression model is integrated into the VM system to create an ensemble VM predictor that can handle multiple products of different small batch sizes. Last, the VM model is repeatedly evaluated to check the performance of the retraining procedure when the model needs to be renewed between production maintenance periods.
Data quality plays an important role during the training stage of machine/deep learning models. The annotation hinges on the experiences of domain experts. To acquire the experts knowledge in the ...context of machine learning, manual data labeling, a tedious and time-consuming task in supervised learning, should be given a top priority. However, the domain experts in the line of plentiful manual annotation may easily get distracted or fatigued after long-time work, causing judgment errors, mislabeling, etc. The pattern recognition of wafer defect map is investigated in this paper, the primary goal of which is to train the convolutional neural network (CNN) model through a very limited number of manually labeled data so that the trained model is capable of performing pseudo labeling. Subsequently, a self-assured adaptive ensemble learner in terms of a series of shallow neural networks is proposed to filter wafer map samples with untrusted pseudo-labels. In the result, the amount of human annotations is significantly reduced by 61% for training a highly accurate classifier. A minimum number of manually labeled data is suggested while the equally high classification performance of wafer defect pattern is maintained. For the evaluation purpose, the proposed self-assured learning is compared with the confidence learning.
Background and Aims
Hepatitis B virus (HBV) infection is a major cause of hepatocellular carcinoma (HCC) development and progression. The aim of this study was to mechanistically investigate the ...involvement of Hippo signalling in HBV surface antigen (HBsAg)‐dependent neoplastic transformation.
Methods
Liver tissue and hepatocytes from HBsAg‐transgenic mice were examined for the Hippo cascade and proliferative events. Functional experiments in mouse hepatoma cells included knockdown, overexpression, luciferase reporter assays and chromatin immunoprecipitation. Results were validated in HBV‐related HCC biopsies.
Results
Hepatic expression signatures in HBsAg‐transgenic mice correlated with YAP responses, cell cycle control, DNA damage and spindle events. Polyploidy and aneuploidy occurred in HBsAg‐transgenic hepatocytes. Suppression and inactivation of MST1/2 led to the loss of YAP phosphorylation and the induction of BMI1 expression in vivo and in vitro. Increased BMI1 directly mediated cell proliferation associated with decreased level of p16INK4a, p19ARF, p53 and Caspase 3 as well as increased Cyclin D1 and γ‐H2AX expression. Chromatin immunoprecipitation and the analysis of mutated binding sites in dual‐luciferase reporter assays confirmed that the YAP/TEAD4 transcription factor complex bound and activated the Bmi1 promoter. In chronic hepatitis B patients, paired liver biopsies of non‐tumour and tumour tissue indicated a correlation between YAP expression and the abundance of BMI1. In a proof‐of‐concept, treatment of HBsAg‐transgenic mice with YAP inhibitor verteporfin directly suppressed the BMI1‐related cell cycle.
Conclusion
HBV‐associated proliferative HCC might be related to the HBsAg‐YAP‐BMI1 axis and offer a potential target for the development of new therapeutic approaches.
Two new two-grid algorithms are proposed for solving the Maxwell eigenvalue problem. The new methods are based on the two-grid methodology recently proposed by Xu and Zhou Math. Comp., 70 (2001), pp. ...17–25 and further developed by Hu and Cheng Math. Comp., 80 (2011), pp. 1287–1301 for elliptic eigenvalue problems. The new two-grid schemes reduce the solution of the Maxwell eigenvalue problem on a fine grid to one linear indefinite Maxwell equation on the same fine grid and an original eigenvalue problem on a much coarser grid. The new schemes, therefore, save total computational cost. The error estimates reveals that the two-grid methods maintain asymptotically optimal accuracy, and the numerical experiments presented confirm the theoretical results.
Purpose:
To characterize the performance of a novel radiation therapy monitoring technique that utilizes a flexible scintillating film, common optical detectors, and image processing algorithms for ...real‐time beam visualization (RT‐BV).
Methods:
Scintillating films were formed by mixing Gd2O2S:Tb (GOS) with silicone and casting the mixture at room temperature. The films were placed in the path of therapeutic beams generated by medical linear accelerators (LINAC). The emitted light was subsequently captured using a CMOS digital camera. Image processing algorithms were used to extract the intensity, shape, and location of the radiation field at various beam energies, dose rates, and collimator locations. The measurement results were compared with known collimator settings to validate the performance of the imaging system.
Results:
The RT‐BV system achieved a sufficient contrast‐to‐noise ratio to enable real‐time monitoring of the LINAC beam at 20 fps with normal ambient lighting in the LINAC room. The RT‐BV system successfully identified collimator movements with sub‐millimeter resolution.
Conclusions:
The RT‐BV system is capable of localizing radiation therapy beams with sub‐millimeter precision and tracking beam movement at video‐rate exposure.
Chloroplastic m-type thioredoxins (TRX m) are essential redox regulators in the light regulation of photosynthetic metabolism. However, recent genetic studies have revealed novel functions for TRX m ...in meristem development, chloroplast morphology, cyclic electron flow, and tetrapyrrole synthesis. The focus of this study is on the putative role of TRX m1, TRX m2, and TRX m4 in the biogenesis of the photosynthetic apparatus in Arabidopsis (Arabidopsis thaliana). To that end, we investigated the impact of single, double, and triple TRX m deficiency on chloroplast development and the accumulation of thylakoid protein complexes. Intriguingly, only inactivation of three TRX m genes led to pale-green leaves and specifically reduced stability of the photosystem II (PSII) complex, implying functional redundancy between three TRX m isoforms. In addition, plants silenced for three TRX m genes displayed elevated levels of reactive oxygen species, which in turn interrupted the transcription of photosynthesis-related nuclear genes but not the expression of chloroplast-encoded PSII core proteins. To dissect the function of TRX m in PSII biogenesis, we showed that TRX m1, TRX m2, and TRX m4 interact physically with minor PSII assembly intermediates as well as with PSII core subunits D1, D2, and CP47. Furthermore, silencing three TRX m genes disrupted the redox status of intermolecular disulfide bonds in PSII core proteins, most notably resulting in elevated accumulation of oxidized CP47 oligomers. Taken together, our results suggest an important role for TRX m1, TRX m2, and TRX m4 proteins in the biogenesis of PSII, and they appear to assist the assembly of CP47 into PSII.
To monitor process and identify the deviation as early as possible, data-driven methods have been applied for process monitoring and fault detection in semiconductor manufacturing. Although various ...fault detection and classification models had been discussed in the literature, however, little research has been devoted to feature selection from trace data that is important for process monitoring of natural variation. Additionally, the high-mix production mode with different recipes leads to process dynamic of wafer-to-wafer (W2W) variation which should also be identified for safeguarding false alarms and serving as a warning indicator. Therefore, this paper proposes a data-driven framework to identify the key features with respect to the W2W variation. In particular, the self-organizing map is used to annotate the grade of wafer variation among the in-line metrology data. Subsequently, the adaptive boosting (AdaBoost) is adopted to examine the effectiveness of every feature and its processing times, respectively. To validate the proposed framework, an empirical study from a semiconductor fabrication plant is conducted. The experimental results demonstrate that the key feature identification is of critical importance to build highly capable models for process monitoring. Through the dimensionality reduction technique, it has been illustrated that a smaller set of the identified key features are able to pinpoint the W2W variation of different wafer grades more clearly than the whole set of process features.
The nonlinear response associated with the current dependence of the superconducting kinetic inductance was studied in capacitively shunted NbTiN microstrip transmission lines. It was found that the ...inductance per unit length of one microstrip line could be changed by up to 20% by applying a dc current, corresponding to a single-pass time delay of 0.7 ns. To investigate nonlinear dissipation, Bragg reflectors were placed on either end of a section of this type of transmission line, creating resonances over a range of frequencies. From the change in the resonance linewidth and amplitude with dc current, the ratio of the reactive to the dissipative response of the line was found to be 788. The low dissipation makes these transmission lines suitable for a number of applications that are microwave- and millimeter-wave band analogs of nonlinear optical processes. As an example, by applying a millimeter-wave pump tone, very-wide-band parametric amplification was observed between about 3 and 34 GHz. Use as a current variable delay line for an on-chip millimeter-wave Fourier transform spectrometer is also considered.
The accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is a major clinical challenge. We developed a model to diagnose IPF by applying Bayesian probit regression (BPR) modelling to gene ...expression profiles of whole lung tissue.
Whole lung tissue was obtained from patients with idiopathic pulmonary fibrosis (IPF) undergoing surgical lung biopsy or lung transplantation. Controls were obtained from normal organ donors. We performed cluster analyses to explore differences in our dataset. No significant difference was found between samples obtained from different lobes of the same patient. A significant difference was found between samples obtained at biopsy versus explant. Following preliminary analysis of the complete dataset, we selected three subsets for the development of diagnostic gene signatures: the first signature was developed from all IPF samples (as compared to controls); the second signature was developed from the subset of IPF samples obtained at biopsy; the third signature was developed from IPF explants. To assess the validity of each signature, we used an independent cohort of IPF and normal samples. Each signature was used to predict phenotype (IPF versus normal) in samples from the validation cohort. We compared the models' predictions to the true phenotype of each validation sample, and then calculated sensitivity, specificity and accuracy.
Surprisingly, we found that all three signatures were reasonably valid predictors of diagnosis, with small differences in test sensitivity, specificity and overall accuracy.
This study represents the first use of BPR on whole lung tissue; previously, BPR was primarily used to develop predictive models for cancer. This also represents the first report of an independently validated IPF gene expression signature. In summary, BPR is a promising tool for the development of gene expression signatures from non-neoplastic lung tissue. In the future, BPR might be used to develop definitive diagnostic gene signatures for IPF, prognostic gene signatures for IPF or gene signatures for other non-neoplastic lung disorders such as bronchiolitis obliterans.
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