BackgroundPrimary insomnia can severely impair daytime function by disrupting attention and working memory and imposes a danger to self and others by increasing the risk of accidents. We speculated that the neurobiological changes impeding working memory in primary insomnia patients would be revealed by resting-state functional MRI (R-fMRI), which estimates the strength of cortical pathways by measuring local and regional correlations in blood oxygen level dependent (BOLD) signs independent of specific task demands.MethodsWe compared the R-fMRI activity patterns of 15 healthy controls to 15 primary insomnia patients (all 30 participants were right-handed) using a 3.0 T MRI scanner. The SPM8 and REST1.7 software packages were used for preprocessing and analysis. Activity was expressed relative to the superior parietal lobe (SPL, the seed region) to reveal differences in functional connectivity to other cortical regions implicated in spatial working memory.ResultIn healthy controls, bilateral SPL activity was associated with activity in the posterior cingulate gyrus, precuneus, ventromedial prefrontal cortex, and superior frontal gyrus, indicating functional connectivity between these regions. Strong functional connectivity between the SPL and bilateral pre-motor cortex, bilateral supplementary motor cortex, and left dorsolateral prefrontal cortex was observed in both the control group and the primary insomnia group. However, the strength of several other functional connectivity pathways to the SPL exhibited significant group differences. Compared to healthy controls, connectivity in the primary insomnia group was stronger between the bilateral SPL and the right ventral anterior cingulate cortex, left ventral posterior cingulate cortex, right splenium of the corpus callosum, right pars triangularis (right inferior frontal gyrus/Broca’s area), and right insular lobe, while connectivity was weaker between the SPL and right superior frontal gyrus (dorsolateral prefrontal cortex).ConclusionPrimary insomnia appears to alter the functional connectivity between the parietal and frontal lobes, cortical structures critical for spatial and verbal working memory.
The use of sludge fermentative short-chain fatty acids (SCFA) as an additional carbon source of biological nutrient removal (BNR) has drawn much attention recently as it can reuse sludge organics, reduce waste activated sludge production, and improve BNR performance. Our previous laboratory study had shown that the SCFA production was significantly enhanced by controlling sludge fermentation at pH 10 with NaOH. This paper focused on a pilot-scale study of alkaline fermentation of waste activated sludge, separation of the fermentation liquid from the alkaline fermentation system, and application of the fermentation liquid to improve municipal biological nitrogen and phosphorus removal. NaOH and Ca(OH)(2) were used respectively to adjust the alkaline fermentation pH, and their effects on sludge fermentation and fermentation liquid separation were compared. The results showed that the use of Ca(OH)(2) had almost the same effect on SCFA production improvement and sludge volatile suspended solids reduction as that of NaOH, but it exhibited better sludge dewatering, lower chemical costs, and higher fermentation liquid recovery efficiency. When the fermentation liquids, adjusted with Ca(OH)(2) and NaOH respectively, were added continuously to an anaerobic-anoxic-aerobic municipal wastewater BNR system, both the nitrogen and phosphorus removals, compared with the control, were improved to the same levels. This was attributed to the increase of not only influent COD but also denitrifying phosphorus removal capability. It seems that the use of Ca(OH)(2) to control sludge fermentation at pH 10 for efficiently producing a carbon source for BNR is feasible.
With multi-core processors becoming popular, exploiting their computational potential becomes an urgent matter. The functionality of multiple standalone computer systems can be aggregated into a single hardware computer by virtualization, giving efficient usage of the hardware and decreased cost for power. Some principles of operating systems can be applied directly to virtual machine systems, however virtualization disrupts the basis of spinlock synchronization in the guest operating system, which results in performance degradation of concurrent workloads such as parallel programs or multi-threaded programs in virtual machines.Eliminating this negative influence of virtualization on synchronization seems to be a non-trivial challenge, especially for concurrent workloads. In this work, we first demonstrate with parallel benchmarks that virtualization can cause long waiting times for spinlock synchronization in the guest operating system, resulting in performance degradation of parallel programs in the virtualized system. Then we propose an adaptive dynamic coscheduling approach to mitigate the performance degradation of concurrent workloads running in virtual machines, while keeping the performance of non-concurrent workloads. For this purpose, we build an adaptive scheduling framework with a series of algorithms to dynamically detect the occurrence of spinlocks with long waiting times, and determine and execute coscheduling of virtual CPUs on physical CPUs in the virtual machine monitor. We have implemented a prototype (ASMan) based on Xen and Linux. Experiments show that ASMan achieves better performance for concurrent workloads, while maintaining the performance for non-concurrent workloads. ASMan coscheduling depends directly on the dynamic behavior of virtual CPUs, unlike other approaches which depend on static properties of workloads and manual setting of rules. Therefore, ASMan achieves a better trade-off between coscheduling and non-coscheduling in the virtual machine monitor, and is an effective solution to this open issue.
Purpose
To explore imaging biomarkers that can be used for diagnosis and prediction of pathologic stage in non-small cell lung cancer (NSCLC) using multiple machine learning algorithms based on CT image feature analysis.
Methods
Patients with stage IA to IV NSCLC were included, and the whole dataset was divided into training and testing sets and an external validation set. To tackle imbalanced datasets in NSCLC, we generated a new dataset and achieved equilibrium of class distribution by using SMOTE algorithm. The datasets were randomly split up into a training/testing set. We calculated the importance value of CT image features by means of mean decrease gini impurity generated by random forest algorithm and selected optimal features according to feature importance (mean decrease gini impurity > 0.005). The performance of prediction model in training and testing sets were evaluated from the perspectives of classification accuracy, average precision (AP) score and precision-recall curve. The predictive accuracy of the model was externally validated using lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples from TCGA database.
Results
The prediction model that incorporated nine image features exhibited a high classification accuracy, precision and recall scores in the training and testing sets. In the external validation, the predictive accuracy of the model in LUAD outperformed that in LUSC.
Conclusions
The pathologic stage of patients with NSCLC can be accurately predicted based on CT image features, especially for LUAD. Our findings extend the application of machine learning algorithms in CT image feature prediction for pathologic staging and identify potential imaging biomarkers that can be used for diagnosis of pathologic stage in NSCLC patients.
Electronic supplementary material
The online version of this article (10.1186/s12885-019-5646-9) contains supplementary material, which is available to authorized users.
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