Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma

PurposeWe have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data.MethodsOur method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data. The diaphragm is detected along a straight line, which was chosen to avoid 4DCT artifact. In the tracking stage, the tumor position on the fluoroscopic images is calculated by applying the model to the diaphragm. Using data from seven liver cases, we evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error. We measured tumor tracking error for the 15 fluoroscopic sequences from the cases and recorded the computation time.ResultsThe mean positional error in diaphragm tracking was 0.57 ± 0.62 mm. The mean positional error in tumor tracking in three-dimensional (3D) space was 0.63 ± 0.30 mm by modeling error, and 0.81–2.37 mm with 1–2 mm setup error. The mean positional error in tumor tracking in the fluoroscopy sequences was 1.30 ± 0.54 mm and the mean computation time was 69.0 ± 4.6 ms and 23.2 ± 1.3 ms per frame for the training and tracking stages, respectively.ConclusionsOur markerless tracking method successfully estimated tumor positions. We believe our results will be useful in increasing treatment accuracy for liver cases.     

Segmentation of left ventricle in short-axis echocardiographic sequences by weighted radial edge filtering and adaptive recovery of dropout regions

In this paper, we present a weighted radial edge filtering algorithm with adaptive recovery of dropout regions for the semi-automatic delineation of endocardial contours in short-axis echocardiographic image sequences. The proposed algorithm requires minimal user intervention at the end diastolic frame of the image sequence for specifying the candidate points of the contour. The region of interest is identified by fitting an ellipse in the region defined by the specified points. Subsequently, the ellipse centre is used for originating the radial lines for filtering. A weighted radial edge filter is employed for the detection of edge points. The outliers are corrected by global as well as local statistics. Dropout regions are recovered by incorporating the important temporal information from the previous frame by means of recursive least squares adaptive filter. This ensures fairly accurate segmentation of the cardiac structures for further determination of the functional cardiac parameters. The proposed algorithm was applied to 10 data-sets over a full cardiac cycle and the results were validated by comparing computer-generated boundaries to those manually outlined by two experts using Hausdorff distance (HD) measure, radial mean square error (rmse) and contour similarity index. The rmse was 1.83 mm with a HD of 5.12 ± 1.21 mm. We have also compared our results with two existing approaches, level set and optical flow. The results indicate an improvement when compared with ground truth due to incorporation of temporal clues. The weighted radial edge filtering algorithm in conjunction with adaptive dropout recovery offers semi-automatic segmentation of heart chambers in 2D echocardiography sequences for accurate assessment of global left ventricular function to guide therapy and staging of the cardiovascular diseases.     

Estimation of wine characteristics using a modified Heliothermal Index in Baden-Württemberg,SW Germany

The Heliothermal Index (HI) is one of the most common methods used to identify suitable areas for viticulture, using sums of air temperatures and latitude of locations. For this study, the HI and a modified version of it was applied to estimate annual wine characteristics in the federal state of Baden-Württemberg in southwest Germany. For this process, measurement data of 11 climate stations from 1960 to 2010 were used. In order to develop a method, 40 years of observation were selected and split into 30 years for developing an estimation method and 10 years for validation. The HI was modified by varying the time periods with which the index was calculated. Time periods with little effect on wine characteristics were identified and excluded to improve the estimation. The must density could be estimated with a bias of about 1 °Oe absolute value, which equals 0.21 °Brix, a mean average error (MAE) of about 4 °Oe / 0.82 °Brix and a root mean square error (RMSE) of about 5 °Oe / 1.03 ° Brix. The titratable acidity could be estimated with a bias of about 0.1 g/L absolute value, a MAE of about 0.7 g/L and a RMSE of about 0.9 g/L.     

Variations in Quercus sp. pollen seasons (1996–2011) in Poznań, Poland,in relation to meteorological parameters

The aim of this study is to supply detailed information about oak (Quercus sp.) pollen seasons in Poznań, Poland, based on a 16-year aerobiological data series (1996–2011). The pollen data were collected using a volumetric spore trap of the Hirst design located in Poznań city center. The limits of the pollen seasons were calculated using the 95 % method. The influence of meteorological parameters on temporal variations in airborne pollen was examined using correlation analysis. Start and end dates of oak pollen seasons in Poznań varied markedly from year-to-year (14 and 17 days, respectively). Most of the pollen grains (around 75 % of the seasonal pollen index) were recorded within the first 2 weeks of the pollen season. The tenfold variation was observed between the least and the most intensive pollen seasons. These fluctuations were significantly related to the variation in the sum of rain during the period second fortnight of March to first fortnight of April the year before pollination (r = 0.799; p < 0.001). During the analyzing period, a significant advance in oak pollen season start dates was observed (?0.55 day/year; p = 0.021), which was linked with an increase in the mean temperature during the second half of March and first half of April (+0.2 °C; p = 0.014). Daily average oak pollen counts correlated positively with mean and maximum daily temperatures, and negatively with daily rainfall and daily mean relative humidity.     

Age-stage two-sex life table analysis of Sesamia nonagrioides (Lep.: Noctuidae) reared on different host plants

The effects of maize and sugarcane were studied on the demographic parameters of Sesamia nonagrioides under controlled environmental conditions. Life table parameters were estimated using age-stage two-sex life table method. Total developmental times of immature stages were 53.51 ± 0.42 and 39.58 ± 0.14 days on maize and sugarcane, respectively. The fecundity values were found to be 93.07 ± 2.94 and 122.95 ± 6.72 eggs/female on maize and sugarcane, respectively. Based on the analysis, the intrinsic rates of increase (r) and the mean generation time (T) of S. nonagrioides were determined as 0.0909 ± 0.0027 and 0.0579 ± 0.0021 day^?1; 42.09 ± 0.21 and 56.83 ± 0.63 days on sugarcane and maize, respectively. Consequently, the sugarcane was a better host for development and population increase of S. nonagrioides than maize. These results can be used to predict population dynamics of the pest on the natural hosts and to improve mass rearing technique on natural diets in insectariums.     

Studies on Core-Shell Nanocapsules of Felodipine: <Emphasis Type="Italic">In Vitro</Emphasis>-<Emphasis Type="Italic">In Vivo</Emphasis> Evaluations

The present study aimed for in vitro-in vivo-in silico simulation studies of experimentally designed (3²-factorial) Capmul PG-8-cored, Eudragit RSPO-Lutrol F 127 nanocapsules to ferry felodipine using GastroPlus?. The in silico parameter sensitivity analysis for pharmacokinetic parameters was initially assessed to justify the preparation of felodipine-loaded nanocapsules (FLNs) with enhanced solubility to overcome the bioavailability issues of felodipine. The overall integrated desirability ranged between 0.8187 and 0.9488 for three optimized FLNs when analyzed for mean particle size, zeta potential, encapsulation efficiency, and in vitro dissolution parameters. The morphological evaluation (SEM, TEM, and AFM) demonstrated spherical nanoparticles (200–300 nm). Validated LC-MS/MS analysis demonstrated enhanced relative bioavailability (13.37-fold) of optimized FLN as compared to suspension. The simulated regional absorption of the FLN presented significant absorption from the cecum (26.3%) and ascending colon (20.1%) with overall absorption of 67.4% from the GIT tract. Furthermore, in vitro-in vivo correlation demonstrated the Wagner-Nelson method as the preferred model as compared to mechanistic and numerical deconvolution on the basis of least mean absolute prediction error, least standard error of prediction, least mean absolute error, and maximum correlation coefficient (r ² = 0.920). The study demonstrated enhanced oral absorption of felodipine-loaded nanocapsules, and GastroPlus? was found to be an efficient simulation tool for in vitro-in vivo-in silico simulations.     

Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep

Elevated asleep heart rate (HR) is a risk factor for cardiovascular disease and other-cause morbidity and mortality. We assessed the accuracy of Fitbit Inc. PurePulse® photoplethysmography with reference to three-lead electrocardiography (ECG) in determining HR during sleep. HR of 35 (17 female) healthy adults 25.1 ± 10.6 years of age (mean ± SD) was continuously recorded throughout a single night of sleep. There was no significant difference in asleep HR mean (0.09 beats per minute [bpm], P = 0.426) between Fitbit photoplethysmography and ECG; plus, there was excellent intraclass correlation (0.998) and narrow Bland–Altman agreement range (2.67 bpm). The regression analysis of Bland–Altman plot of mean asleep HR indicates Fitbit tends to slightly overestimate reference values in the lower range of HR (HR < 50 bpm) by 0.51 bpm and slightly underestimate reference values in the higher range of HR (HR > 80 bpm) by 0.63 bpm. Mixed model analysis of epoch-by-epoch (5-min epochs) asleep HR showed significant “U” shape trend (P < 0.001) in amount of Fitbit error (absolute amount of difference between ECG and Fitbit values regardless of overestimation or underestimation) in regard to HR, i.e. smaller error in the medium range of HR (60–80 bpm) and slightly larger error for lower (<60 bpm) and higher (>80 bpm) ranges of HR. However, effect of age, body mass index, gender, and subjective sleep quality measured by Pittsburgh sleep quality index (good/poor sleepers) on error in estimating HR by the Fitbit method was not significant. It is concluded that Fitbit photoplethysmography suitably tracks HR during sleep in healthy young adults.     

Preclinical pharmacokinetics of MHAA4549A,a human monoclonal antibody to influenza A virus,and the prediction of its efficacious clinical dose for the treatment of patients hospitalized with influenza A

MHAA4549A is a human immunoglobulin G1 (IgG1) monoclonal antibody that binds to a highly conserved epitope on the stalk of influenza A hemagglutinin and blocks the hemagglutinin-mediated membrane fusion in the endosome, neutralizing all known human influenza A strains. Pharmacokinetics (PK) of MHAA4549A and its related antibodies were determined in DBA/2J and Balb-c mice at 5 mg/kg and in cynomolgus monkeys at 5 and 100 mg/kg as a single intravenous dose. Serum samples were analyzed for antibody concentrations using an ELISA and the PK was evaluated using WinNonlin software. Human PK profiles were projected based on the PK in monkeys using species-invariant time method. The human efficacious dose projection was based on in vivo nonclinical pharmacological active doses, exposure in mouse infection models and expected human PK. The PK profiles of MHAA4549A and its related antibody showed a linear bi-exponential disposition in mice and cynomolgus monkeys. In mice, clearance and half-life ranged from 5.77 to 9.98 mL/day/kg and 10.2 to 5.76 days, respectively. In cynomolgus monkeys, clearance and half-life ranged from 4.33 to 4.34 mL/day/kg and 11.3 to 11.9 days, respectively. The predicted clearance in humans was ～2.60 mL/day/kg. A single intravenous dose ranging from 15 to 45 mg/kg was predicted to achieve efficacious exposure in humans. In conclusion, the PK of MHAA4549A was as expected for a human IgG1 monoclonal antibody that lacks known endogenous host targets. The predicted clearance and projected efficacious doses in humans for MHAA4549A have been verified in a Phase 1 study and Phase 2a study, respectively.     

A Rapid Method,Combining Microwave-Assisted Extraction and Gas Chromatography-Mass Spectrometry with a Database,for Determining Organochlorine Pesticides and Polycyclic Aromatic Hydrocarbons in Soils and Sediments

A rapid method for determining organochlorine pesticides and polycyclic aromatic hydrocarbons in soils and sediments was developed to allow pollution surveys to be performed in emergencies. The method involves microwave-assisted extraction and uses an automated identification/quantification system with a gas chromatography mass spectrometry database. A sample (3 g) is extracted with a 3:2 v/v hexane:water mixture (10 mL) for 30 min using a microwave-assisted extraction system at 120°C. The hexane extract is then cleaned using silica gel, then analyzed by gas chromatography mass spectrometry. The total analysis time is approximately 4 h. The precision of the quantitative results and accuracy of the analyte identification were determined. The total analyte concentrations were generally comparable to (61%–110% of) the concentrations determined using a Soxhlet extraction method, but the concentrations of individual high-molecular-weight polycyclic aromatic hydrocarbons were unacceptably low compared with the concentrations determined using the Soxhlet method. However, these compounds (e.g., benzo(ghi)perylene and indeno(1,2,3-cd)pyrene) were subsequently efficiently extracted using a hexane:water:ethanol mixture. The accuracy of identification was evaluated using accurate masses determined by gas chromatography time-of-flight mass spectrometry, and the mass error was 2 ppm for 21 of the 22 compounds identified using the new method.     

In-Line Film Coating Thickness Estimation of Minitablets in a Fluid-Bed Coating Equipment

Film coating thickness of minitablets was estimated in-line during coating in a fluid-bed equipment by means of visual imaging. An existing, commercially available image acquisition system was used for image acquisition, while dedicated image analysis and data analysis methods were developed for this purpose. The methods were first tested against simulated minitablet’s images and after that examined on a laboratory-scale fluid-bed Wurster coating process. An observation window cleaning mechanism was developed for this purpose. Six batches of minitablets were coated in total, using two different dispersions, where for the second dispersion coating endpoint was determined based on the in-line measurement. Coating thickness estimates were calculated from the increasing size distributions of the minitablet’s major and minor lengths, assessed from the acquired images. Information on both the minitablet’s average band and average cap coating thicknesses was obtained. The in-line coating thickness estimates were compared to the coating thickness weight gain calculations and the optical microscope measurements as a reference method. Average band coating thickness estimate was found the most accurate in comparison to microscope measurements, with root mean square error of 1.30 μm. The window cleaning mechanism was crucial for the accuracy of the in-line measurements as was evident from the corresponding decrease of the root mean square error (9.52 μm, band coating thickness). The presented visual imaging approach exhibits accuracy of at least 2 μm and is not susceptible to coating formulation or color variations. It presents a promising alternative to other existing techniques for the in-line coating thickness estimation.     

Propagating Uncertainty in Plot-based Estimates of Forest Carbon Stock and Carbon Stock Change

Ecosystem science increasingly relies on highly derived metrics to synthesize across large datasets. However, full uncertainty associated with these metrics is seldom quantified. Our objective was to evaluate measurement error and model uncertainty in plot-based estimates of carbon stock and carbon change. We quantified the measurement error associated with live stems, deadwood and plot-level variables in temperate rainforest in New Zealand. We also quantified model uncertainty for height–diameter allometry, stem volume equations and wood-density estimates. We used Monte Carlo simulation to assess the net effects on carbon stock and carbon change estimated using data from 227 plots from throughout New Zealand. Plot-to-plot variation was the greatest source of uncertainty, amounting to 9.1% of mean aboveground carbon stock estimates (201.11 MgC ha^?1). Propagation of the measurement error and model uncertainty resulted in a 1% increase in uncertainty (0.1% of mean stock estimate). Carbon change estimates (mean ?0.86 MgC ha^?1 y^?1) were more uncertain, with sampling error equating to 56% of the mean, and when measurement error and model uncertainty were included this uncertainty increased by 35% (22.1% of the mean change estimate). For carbon change, the largest sources of measurement error were missed/double counted stems and fallen coarse woody debris. Overall, our findings show that national-scale plot-based estimates of carbon stock and carbon change in New Zealand are robust to measurement error and model uncertainty. We recommend that calculations of carbon stock and carbon change incorporate both these sources of uncertainty so that management implications and policy decisions can be assessed with the appropriate level of confidence.     

The speciation history and systematics of Carthamus (Asteraceae) with special emphasis on Turkish species by integrating phylogenetic and Ecological Niche Modelling data

To discuss the classification and possible scenarios for the speciation of Carthamus species in Turkey, 143 species occurrence data from Turkey used in Ecological Niche Modelling (ENM), ITS sequences of 23 available species gathered from the GenBank and current distribution information were used. The ENM was carried out by using MAXENT software. Among the 19 bioclimatic variables used in ENM, precipitation of coldest quarter (25 %), mean temperature of driest quarter (19 %) and annual precipitation (17 %) parameters have the highest percent contribution to the resulting prediction pattern, respectively. Bayesian-based phylogenetic analysis with divergence time estimation was implemented to obtain phylogenetic history of Carthamus species. Statistical dispersal–vicariance analysis and Bayesian binary MCMC analysis were also used to discuss biogeographical inferences. An identification key for Turkish Carthamus species that is in accordance with phylogenies was given. Ancestral area reconstruction analyses pointed out that the Western Asia region was the ancestral area for Carthamus species and in the Pliocene/Pleistocene period they started to diversify. Also ENM results clearly indicate that especially Anatolian species used Aegean and Mediterranean coastal part of Anatolia as potential refugia.     

Understanding ageing effects using complexity analysis of foot–ground clearance during walking

Ageing influences gait patterns which in turn can affect the balance control of human locomotion. Entropy-based regularity and complexity measures have been highly effective in analysing a broad range of physiological signals. Minimum toe clearance (MTC) is an event during the swing phase of the gait cycle and is highly sensitive to the spatial balance control properties of the locomotor system. The aim of this research was to investigate the regularity and complexity of the MTC time series due to healthy ageing and locomotors' disorders. MTC data from 30 healthy young (HY), 27 healthy elderly (HE) and 10 falls risk (FR) elderly subjects with balance problems were analysed. Continuous MTC data were collected and using the first 500 data points, MTC mean, standard deviation (SD) and entropy-based complexity analysis were performed using sample entropy (SampEn) for different window lengths (m) and filtering levels (r). The MTC SampEn values were lower in the FR group compared to the HY and HE groups for all m and r. The HY group had a greater mean SampEn value than both HE and FR reflecting higher complexity in their MTC series. The mean SampEn values of HY and FR groups were found significantly different for m = 2, 4, 5 and r = (0.1–0.9) × SD, (0.3–0.9) × SD and (0.3–0.9) × SD, respectively. They were also significant difference between HE and FR groups for m = 4–5 and r = (0.3–0.7) × SD, but no significant differences were seen between HY and HE groups for any m and r. A significant correlation of SampEn with SD of MTC was revealed for the HY and HE groups only, suggesting that locomotor disorders could significantly change the regularity or the complexity of the MTC series while healthy ageing does not. These results can be usefully applied to the early diagnosis of common gait pathologies.     

Accurate PDZ/Peptide Binding Specificity with Additive and Polarizable Free Energy Simulations

PDZ domains contain 80–100 amino acids and bind short C-terminal sequences of target proteins. Their specificity is essential for cellular signaling pathways. We studied the binding of the Tiam1 PDZ domain to peptides derived from the C-termini of its Syndecan-1 and Caspr4 targets. We used free energy perturbation (FEP) to characterize the binding energetics of one wild-type and 17 mutant complexes by simulating 21 alchemical transformations between pairs of complexes. Thirteen complexes had known experimental affinities. FEP is a powerful tool to understand protein/ligand binding. It depends, however, on the accuracy of molecular dynamics force fields and conformational sampling. Both aspects require continued testing, especially for ionic mutations. For six mutations that did not modify the net charge, we obtained excellent agreement with experiment using the additive, AMBER ff99SB force field, with a root mean square deviation (RMSD) of 0.37 kcal/mol. For six ionic mutations that modified the net charge, agreement was also good, with one large error (3 kcal/mol) and an RMSD of 0.9 kcal/mol for the other five. The large error arose from the overstabilization of a protein/peptide salt bridge by the additive force field. Four of the ionic mutations were also simulated with the polarizable Drude force field, which represents the first test of this force field for protein/ligand binding free energy changes. The large error was eliminated and the RMS error for the four mutations was reduced from 1.8 to 1.2 kcal/mol. The overall accuracy of FEP indicates it can be used to understand PDZ/peptide binding. Importantly, our results show that for ionic mutations in buried regions, electronic polarization plays a significant role.     

A neural network model for simulation of water levels at the Sultan Marshes wetland in Turkey

An artificial neural network (ANN) model was developed for simulating water levels at the Sultan Marshes in Turkey. Sultan Marshes is a closed basin wetland located in the semi-arid Central Anatolia region of Turkey. It is one of the thirteen Ramsar sites of Turkey and a national park. Water levels at the Sultan Marshes showed strong fluctuations in recent decades due to the changes in climatic and hydrologic conditions. In this study, monthly average water levels were simulated using a multi-layer perceptron type ANN model. The model inputs consisted of climatic data (precipitation, air temperature, evapotranspiration) and hydrologic data (ground water levels, spring flow rates, and previous month water levels) available from 1993 to 2002. 70 % of the data were used for model training and remaining 30 % were used for model testing. Model training was accomplished by using a scaled conjugate gradient backpropagation algorithm. The performance of the model was evaluated by calculating the root mean square error (RMSE) and the coefficient of determination (R ²) between observed and simulated water levels. The sensitivity of the model to input parameters was determined by evaluating the model performance when a single input variable was excluded. It was found that the ANN model can successfully be used for simulating water levels at the Sultan Marshes. The model developed using all input variables provided the best results with two neurons in the hidden layer. The RMSE and R ² of the simulated water levels were 4.0 cm and 96 %, respectively. The sensitivity analysis showed that the model was most sensitive to previous month water levels and ground water levels.     

A novel method for prediction of dynamic smiling expressions after orthodontic treatment: a case report

Smile esthetics has become increasingly important for orthodontic patients, thus prediction of post-treatment smile is necessary for a perfect treatment plan. In this study, with a combination of three-dimensional craniofacial data from the cone beam computed tomography and color-encoded structured light system, a novel method for smile prediction was proposed based on facial expression transfer, in which dynamic facial expression was interpreted as a matrix of facial depth changes. Data extracted from the pre-treatment smile expression record were applied to the post-treatment static model to realize expression transfer. Therefore smile esthetics of the patient after treatment could be evaluated in pre-treatment planning procedure. The positive and negative mean values of error for prediction accuracy were 0.9 and ? 1.1 mm respectively, with the standard deviation of ± 1.5 mm, which is clinically acceptable. Further studies would be conducted to reduce the prediction error from both the static and dynamic sides as well as to explore automatically combined prediction from the two sides.     

A meta-analysis of the publicly available bacterial and archaeal sequence diversity in saline soils

An integrated view of bacterial and archaeal diversity in saline soil habitats is essential for understanding the biological and ecological processes and exploiting potential of microbial resources from such environments. This study examined the collective bacterial and archaeal diversity in saline soils using a meta-analysis approach. All available 16S rDNA sequences recovered from saline soils were retrieved from publicly available databases and subjected to phylogenetic and statistical analyses. A total of 9,043 bacterial and 1,039 archaeal sequences, each longer than 250 bp, were examined. The bacterial sequences were assigned into 5,784 operational taxonomic units (OTUs, based on ≥97 % sequence identity), representing 24 known bacterial phyla, with Proteobacteria (44.9 %), Actinobacteria (12.3 %), Firmicutes (10.4 %), Acidobacteria (9.0 %), Bacteroidetes (6.8 %), and Chloroflexi (5.9 %) being predominant. Lysobacter (12.8 %) was the dominant bacterial genus in saline soils, followed by Sphingomonas (4.5 %), Halomonas (2.5 %), and Gemmatimonas (2.5 %). Archaeal sequences were assigned to 602 OTUs, primarily from the phyla Euryarchaeota (88.7 %) and Crenarchaeota (11.3 %). Halorubrum and Thermofilum were the dominant archaeal genera in saline soils. Rarefaction analysis indicated that less than 25 % of bacterial diversity, and approximately 50 % of archaeal diversity, in saline soil habitats has been sampled. This analysis of the global bacterial and archaeal diversity in saline soil habitats can guide future studies to further examine the microbial diversity of saline soils.