Prediction of multi-criteria optimization (MCO) parameter efficiency in volumetric modulated arc therapy (VMAT) treatment planning using machine learning (ML)

PurposeTo predict the impact of optimization parameter changes on dosimetric plan quality criteria in multi-criteria optimized volumetric-modulated-arc therapy (VMAT) planning prior to optimization using machine learning (ML).MethodsA data base comprising a total of 21,266 VMAT treatment plans for 44 cranial and 18 spinal patient geometries was generated. The underlying optimization algorithm is governed by three highly composite parameters which model a combination of important aspects of the solution. Patient geometries were parametrized via volume- and shape properties of the voxel objects and overlap-volume histograms (OVH) of the planning-target-volume (PTV) and a relevant organ-at-risk (OAR). The impact of changes in one of the three optimization parameters on the maximally achievable value range of five dosimetric properties of the resulting dose distributions was studied. To predict the extent of this impact based on patient geometry, treatment site, and current parameter settings prior to optimization, three different ML-models were trained and tested. Precision-recall curves, as well as the area-under-curve (AUC) of the resulting receiver-operator-characteristic (ROC) curves were analyzed for model assessment.ResultsSuccessful identification of parameter regions resulting in a high variability of dosimetric plan properties depended on the choice of geometry features, the treatment indication and the plan property under investigation. AUC values between 0.82 and 0.99 could be achieved. The best average-precision (AP) values obtained from the corresponding precision/recall curves ranged from 0.71 to 0.99.ConclusionsMachine learning models trained on a database of pre-optimized treatment plans can help finding relevant optimization parameter ranges prior to optimization.     

Photon beam energy dependent single-arc volumetric modulated arc optimization

PurposeThe purpose of this work was to present a new single-arc mixed photon (6&18MV) VMAT (SAMP) optimization framework that concurrently optimizes for two photon energies with corresponding partial arc lengths.Methods and materialsOwing to simultaneous optimization of energy dependent intensity maps and corresponding arc locations, the proposed model poses nonlinearity. Unique relaxation constraints based on McCormick approximations were introduced for linearization. Energy dependent intensity maps were then decomposed to generate apertures. Feasibility of the proposed framework was tested on a sample of ten prostate cancer cases with lateral separation ranging from 34 cm (case no.1) to 52 cm (case no.6). The SAMP plans were compared against single energy (6MV) VMAT (SE) plans through dose volume histograms (DVHs) and radiobiological parameters including normal tissue complication probability (NTCP) and equivalent uniform dose (EUD).ResultsThe contribution of higher energy photon beam optimized by the algorithm demonstrated an increase for cases with a lateral separation >40 cm. SAMP–VMAT notably improved bladder and rectum sparing in large size cases. Compared to single energy, SAMP–VMAT plans reduced bladder and rectum NTCP in cases with large lateral separation. With the exception of one case, SAMP–VMAT either improved or maintained femoral heads compared to SE–VMAT. SAMP–VMAT reduced the nontarget tissue integral dose in all ten cases.ConclusionsA single-arc VMAT optimization framework comprising mixed photon energy partial arcs was presented. Overall results underline the feasibility and potential of the proposed approach for improving OAR sparing in large size patients without compromising the target homogeneity and coverage.     

Effects and statistical optimization of fermentation conditions on growth and poly (vinyl alcohol)-degrading enzyme production of Streptomyces venezuelae GY1

The effects of environmental conditions, including temperature, pH and dissolved oxygen, on growth and production of polyvinyl alcohol (PVA)-degrading enzymes of the newly-isolated strain Streptomyces venezuelae GY1 were investigated. The medium composition for strain GY1 was studied first by single factorial design and then optimized using a central composite design. PVA with high saponification is better for growth of, and PVA-degrading enzyme production by S. venezuelae GY1 compared with PVA with low saponification, in contrast with the characteristics of other bacteria producing PVA-degrading enzymes. The optimal temperature and initial pH for production of PVA-degrading enzyme by strain GY1 was 30°C and 7.0, respectively. The optimal medium composition for PVA-degrading enzyme production is: 1.01 g L^?1 of PVA1799, 0.307 g L^?1 of NaNO₃ and 0.512 g L^?1 of MgSO₄?7H₂O.     

Bone Load Estimation for the Proximal Femur Using Single Energy Quantitative CT Data

A density-based load estimation method was applied to determine femoral load patterns. Two-dimensional finite element models were constructed using single energy quantitative computed tomography (QCT) data from two femora. basic load cases included parabolic pressure joint loads and constant tractions on the greater trochanter. An optimization procedure adjusted magnitudes of the basic load cases, such that the applied mechanical stimulus approached the ideal stimulus throughout each model. Dominant estimated load directions were generally consistent with published experimental data for gait. Other estimated loads suggested that loads at extreme joint orientations may be important to maintenance of bone structure. Remodeling simulations with the estimated loads produced density distributions qualitatively similar to the QCT data sets. Average nodal density errors between QCT data and predictions were 0·24 g/cm³ and 0·28 g/cm³. The results indicate that density-based load estimation could improve understanding of loading patterns on bones.     

CODON OPTIMIZATION INCREASES HUMAN KALLISTATIN EXPRESSION IN Escherichia coli

A unique serpin, kallistatin, displays vasodilatory, antiangiogenic, anti-inflammatory, and antioxidant activity. Difficulty and low efficacy of obtaining recombinant kallistatin limit the wide investigation of its biological and pathological function. The present study employed a codon optimization algorithm to redesign the kallistatin gene and achieved a high yield of recombinant kallistatin protein. The kallistatin codons were redesigned for a more suitable Escherichia coli host without altering amino acids. Base composition and GC% content were compared between synthetic optimized kallistatin (opti-kallistatin) and wild-type kallistatin (wt-kallistatin). Both opti-kallistatin and wt-kallistatin were purified using Ni-NTA His-binding resins through fast protein liquid chromatography (FPLC). The identity and purity of kallistatin were confirmed by Coomassie blue staining, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and Western blot analysis. The output of opti-kallistatin protein was ～2-fold increase (2.09 ± 0.23 mg/L) compared to wt-kallistatin (1.05 ± 0.2 mg/L). These results suggest that more common codon optimization in the E. coli host significantly increases the yield of heterologous human protein yields. This approach will remarkably facilitate the further investigation of kallistatin in vitro and in vivo.     

MODEL DEVELOPMENT AND PROCESS OPTIMIZATION FOR SOLVENT EXTRACTION OF POLYPHENOLS FROM RED GRAPES USING BOX–BEHNKEN DESIGN

The objective of the present study is to find out the optimum extraction conditions for extraction of polyphenols from red grapes using Box–Behnken design. Red grapes polyphenols were extracted using acid–ethanol solvent at various extraction temperature (40–60°C), extraction time (20–100 min) and different solid–liquid ratio (1:5–1:15 g:ml). The effect (main and interactive) of extraction conditions on total anthocyanin, phenolic and flavonoid content were studied using Box–Behnken design (three factors at three levels). The results showed that the contribution of the quadratic model was significant for all the responses. Second-order mathematical regression models were developed and were found to fit well with observed data. Derringer's desirability function methodology was performed to find out the optimal conditions based on both individual and combinations of all responses (extraction temperature: 57°C, time: 61 min, and solid–liquid ratio: 1:8.7 g:ml) were established. At this optimal condition, the anthocyanin yield, total phenolic and flavonoid content were 73.92 mg/100 g, 221.4 mg GAE/100 g, and 79.08 mg CE/100 g, respectively. A desirability value of 0.902 was achieved at this point.     

A New Method for the Preparation of Minicells for Physiological Studies

A procedure has been developed for preparing minicells that does not rely on sucrose gradients in a rate-zone centrifuge. In the presence of low levels (10 units/ml) of penicillin, the contaminating bacteria present in minicell cultures after low-speed differential centrifugation are turned into long filamentous cells and can be killed by sonic treatment. An additional low-speed centrifugation (2, 000 g for 5 min) yields purified minicells with less than one contaminating cell per 10 minicells.     

STATISTICAL MEDIA OPTIMIZATION FOR THE BIOMASS PRODUCTION OF POSTHARVEST BIOCONTROL YEAST Rhodosporidium paludigenum

A cane molasses-based medium for the biomass production of biocontrol agent Rhodosporidium paludigenum was statistically optimized. Molasses concentration (after pretreatment), yeast extract, and initial pH were identified by the Plackett–Burman design to show significant influence on the biomass production. The three factors were further optimized by central composite design and response-surface methodology. The statistical analysis indicated the optimum values of the variables were 89.98 g/L for cane molasses, 2.35 g/L for yeast extract and an initial pH of 8.48. The biomass yield at the optimal culture achieved 15.89 g/L in flask fermentation, which was 2.1 times higher than that at the initial NYDB medium. In a 10-L fermenter, 18.97 g/L of biomass was obtained after 36 hr of cultivation. Moreover, the biocontrol efficacy of the yeast was investigated after culture optimization. The results showed the yeast harvested in the optimal medium maintained its initial biocontrol properties by reducing the percentage of decayed apples to below 20%.     

Chitin and chitosan biopolymer production from the Iranian medicinal fungus Ganoderma lucidum: Optimization and characterization

Abstract

Chitin and chitosan with unique properties and numerous applications can be produced from fungus. The production of chitin and chitosan from the mycelia of an Iranian Ganoderma lucidum was studied to improve cell growth and chitin productivity. Inoculum size and initial pH as two effective variables on the growth of G. lucidum and chitin production were optimized using response surface method (RSM) by central composite design (CCD). The results verified the significant effect of these two variables on the cell growth and chitin production. In optimum conditions, including pH?=?5.7 and inoculum size of 7.4%, the cell dry weight was 5.91?g/L and the amount of chitin production was 1.08?g/L with the productivity of 0.083?g/(L day). The produced chitin and chitosan were characterized using XRD and FTIR. Moreover, the antibacterial activity of the produced chitosan was investigated and compared with the commercial chitosan. The results showed that the produced chitin and chitosan had suitable quality and the Iranian G. lucidum would be a great source for safe and high-quality chitin and chitosan production.