Notes

A total chloroplast genome associated with Keteleeria davidiana (Pinaceae) and its phylogenetic effects.
Taking the key components of the MAM monitoring system as the mainstream, this study investigates the MAM monitoring system, measurement and signal acquisition, signal and image processing, as well as machine learning methods for the process monitoring and quality classification. The advantages and disadvantages of their algorithmic implementations and applications are discussed and summarized. Finally, the prospects of MAM process monitoring researches are advised.This paper deals with estimation of interval controls for interval Linear Time Invariant (LTI) plants using pole placement technique. Generally, the parameters associated with LTI plant system are assumed to be crisp values, but due to the occurrence of error in experimentation, measurement or due to maintenance induced errors, the parameters are imprecise. As such, the LTI problem reduces to interval LTI. The pole placement problem is then reduced to interval algebraic equation viz. interval Diophantine equation using proper transfer function. Further, the interval Diophantine equation is transformed to Interval System of Linear Equations (ISLE) viz. interval Sylvester matrix equation. Initially, using interval arithmetic an interval algebraic approach has been applied to solve the ISLE for computing non-negative or non-positive controls. Then, another approach using Artificial Neural Network (ANN) is proposed for computing the interval controls. Further, algorithms based on sign function and ANN procedures have been discussed. Finally, the efficiency of the proposed algorithms has been verified using different order interval plants.
To explore changes in left ventricular (LV) function and the relationship of these changes with myocardial blood flow (MBF) evaluated by
N-ammonia hybrid positron-emission tomography (PET)/magnetic resonance imaging (MRI) during vasodilator stress in patients with suspected coronary artery disease (CAD).

Fifty-two consecutive patients with suspected CAD, who underwent
N-ammonia PET/MRI, were enrolled. Vasodilator stress was induced by intravenous injection of adenosine. MBF and coronary flow reserve (CFR) were calculated from dynamic acquisition of
N-ammonia PET. LV function was evaluated by MRI both at rest and during vasodilator stress. selleck inhibitor An abnormal perfusion on myocardial images was defined as a summed difference score of ≥4.

MRI showed that the LV end-diastolic volume, LV end-systolic volume, and LV ejection fraction (LVEF) remained unchanged during vasodilator stress in all patients (n=52) as well as in the patients with CFR of <2 (n=27), stress MBF of <1.3 ml/g/min (n=28), abnormal myocardial perfusion (n=30), and more than one diseased vessel (n=46). In only four patients, the LVEF measured by MRI decreased by >5% during vasodilator stress. In these four patients, CFR was lower (1.57±0.12 versus 2.18±0.86, p<0.01) and the number of diseased vessels was higher (2.75±0.50 versus 1.48±0.92, p<0.01) than in patients without post-stress LV dysfunction.

The LV volume and systolic function evaluated by cardiac MRI remained unchanged during vasodilator stress; however, LV dysfunction during vasodilator stress may occur in patients with severe CAD.
The LV volume and systolic function evaluated by cardiac MRI remained unchanged during vasodilator stress; however, LV dysfunction during vasodilator stress may occur in patients with severe CAD.
To develop and validate a triple-classification radiomics model for the preoperative differentiation of pleomorphic adenoma (PA), Warthin tumour (WT), and malignant salivary gland tumour (MSGT) based on diffusion-weighted imaging (DWI).

Data from 217 patients with histopathologically confirmed salivary gland tumours (100 PAs, 68 WTs, and 49 MSGTs) from January 2015 to March 2019 were analysed retrospectively and divided into a training set (n=173), and a validation set (n=44). A total of 396 radiomic features were extracted from the DWI of all patients. Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression were used to select radiomic features, which were then constructed using three classification models, namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN). The diagnostic performance of the radiomics model was quantified by the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) of the training and validation data sets.

The 20 most valuable features were investigated based on the LASSO regression. LR and SVM methods exhibited better diagnostic ability than KNN for multiclass classification. LR and SVM had the best performance and yielded the AUC values of 0.857 and 0.824, respectively, in the training data set and the AUC values of 0.932 and 0.912, respectively, in the validation data set of MSGT diagnosis.

DWI-based triple-classification radiomics model has predictive value in distinguishing PA, WT, and MSGT, which can be used for preoperative auxiliary diagnosis in clinical practice.
DWI-based triple-classification radiomics model has predictive value in distinguishing PA, WT, and MSGT, which can be used for preoperative auxiliary diagnosis in clinical practice.
To evaluate variation in the pre-pandemic use of endoscopic ultrasound (EUS) for oesophageal cancer diagnosis and treatment planning up to 2019, and which factors contributed to this.

A UK-wide online survey of oesophagogastric multidisciplinary team lead clinicians was undertaken to determine perceptions towards, and the use of, EUS to aid staging and treatment planning in oesophageal cancer.

Thirty-five responses were received, representing 97 UK National Health Service Trusts/Health Boards. A majority of centres (n=21, 60%) did not have formal written guidance for EUS use. Although all respondents had access to EUS, a perceived lack of utility (n=7) and concerns about delaying treatment start dates (n=8) each restricted EUS use for a fifth of respondents. For most centres (n=24, 68.6%), EUS use is case-specific, whereas for 10 (28.6%) EUS is used for all patients with potentially curable disease. A majority of centres use diagnostic positron-emission tomography for radiotherapy target volume delineation (TVD), whereas 22 (62.
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