Automated thresholded region classification using a robust feature selection method for PET-CT

Fluorodeoxyglucose Positron Emission Tomography - Computed Tomography (FDG PET-CT) is the preferred imaging modality for staging the lymphomas. Sites of disease usually appear as foci of increased FDG uptake. Thresholding is the most common method used to identify these regions. The thresholding method, however, is not able to separate sites of FDG excretion and physiological FDG uptake (sFEPU) from sites of disease. sFEPU can make image interpretation problematic and so the ability to identify / label sFEPU will improve image interpretation and the assessment of the total disease burden and will be beneficial for any computer aided diagnosis software. Existing classification methods, however, are sub-optimal as there is a tendency for over-fitting and increased computational burden because they are unable to identify optimal features that can be used for classification. In this study, we propose a new method to delineate sFEPU from thresholded PET images. We propose a feature selection method, which differs from existing approaches, in that it focuses on selecting optimal features from individual structures, rather than from the entire image. Our classification results on 9222 coronal slices derived from 40 clinical lymphoma patient studies produced higher classification accuracy when compared to existing feature selection based methods

Parametric Images in Assessing Bone Grafts Using Dynamic 18F-Fluoride PET

The early identification of graft failure would improve patient management. 18F-fluoride is a suitable tracer for quantifying bone metabolism. Performance of parametric images constructed by Patlak graphical analysis (PGA) with various time periods was evaluated in the analysis of dynamic 18F-fluoride PET studies of eight patients with fibula bone grafts after limb salvage surgery. The PGA parametric image approach tended to underestimate influx rate. The linear portion of PGA analysis was found to be from 10 to 50 min. It shows promise in providing a quantitative assessment of the viability of bone grafts

Development and validation of a nomogram model for predicting unfavorable functional outcomes in ischemic stroke patients after acute phase

IntroductionPrediction of post-stroke functional outcome is important for personalized rehabilitation treatment, we aimed to develop an effective nomogram for predicting long-term unfavorable functional outcomes in ischemic stroke patients after acute phase.MethodsWe retrospectively analyzed clinical data, rehabilitation data, and longitudinal follow-up data from ischemic stroke patients who underwent early rehabilitation at multiple centers in China. An unfavorable functional outcome was defined as a modified Rankin Scale (mRS) score of 3–6 at 90 days after onset. Patients were randomly allocated to either a training or test cohort in a ratio of 4:1. Univariate and multivariate logistic regression analyses were used to identify the predictors for the development of a predictive nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate predictive ability in both the training and test cohorts.ResultsA total of 856 patients (training cohort: n = 684; test cohort: n = 172) were included in this study. Among them, 518 patients experienced unfavorable outcomes 90 days after ischemic stroke. Trial of ORG 10172 in Acute Stroke Treatment classification (p = 0.024), antihypertensive agents use [odds ratio (OR) = 1.86; p = 0.041], 15-day Barthel Index score (OR = 0.930; p < 0.001) and 15-day mRS score (OR = 13.494; p < 0.001) were selected as predictors for the unfavorable outcome nomogram. The nomogram model showed good predictive performance in both the training (AUC = 0.950) and test cohorts (AUC = 0.942).ConclusionThe constructed nomogram model could be a practical tool for predicting unfavorable functional outcomes in ischemic stroke patients underwent early rehabilitation after acute phase

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Neural Network Energy Management Strategy for Series Hybrid Electric Car

A design concept of energy management and control strategy for hybrid electric car based on neural network and global optimization is proposed. The control strategy can effectively combine the advantages of global optimization algorithm and neural network algorithm. The minimum fuel consumption of the engine model can be derived. Simulation and analysis of the known road cycle conditions were carried out. The simulation platform ADVISOR2002 is used for the secondary development. The control strategy, the power monitoring control strategy and the thermostat control strategy were simulated and compared. The strategy has a strong adaptive capacity which can further improve the fuel economy of hybrid car

Spatiotemporal Changes in Water Yield Function and Its Influencing Factors in the Tibetan Plateau in the Past 20 Years

The Tibetan Plateau, known as the “Water Tower of Asia”, has made important contributions to global climate regulation and water conservation. With global climate change and water shortages, the yield and reserves of water on the Tibetan Plateau have undergone obvious changes, and its water yield function and water conservation function have gradually attracted widespread attention. The results show that the total water yield in the past 20 years is 128,403.06 billion m3, spatially reduced from southeast to northwest, and the interannual variation is large but increases slowly overall. The water yield capacity is higher in the areas of less than 3000 m and 3500~4500 m, and it is stronger with the increase in slope. The water yield capacity is extremely strong in the middle and north subtropical zone. Ecological zones with high water yield capacity are mostly covered with woodland and alpine meadows. The precipitation (P) is the dominant factor in the water yield function before actual evapotranspiration (AET) = 500 mm, and then the negative force of AET is enhanced. High altitude inhibits the positive effect of the normalized vegetation index (NDVI), and the water yield at altitudes of less than 3000 m shows an almost linear relationship with the leaf area index (LAI). When LAI > 0.2, the slower the slope, the higher the water yield and the lower the growth rate. The spatial distribution of P change and water yield change is consistent and significantly positively correlated; P and NDVI changes positively affected changes in water yield, while AET and LAI changes had the opposite effect. In summary, combined with topographic factors, this study emphasizes the influence of climate and vegetation changes on the spatiotemporal changes in water yield on the Tibetan Plateau, which can provide a theoretical basis for the assessment and prediction of water yield capacity and water conservation capacity in this area