A screening model to predict microalgae biomass growth in photobioreactors and raceway ponds

A microalgae biomass growth model was developed for screening novel strains for their potential to exhibit high biomass productivities under nutrient‐replete conditions in photobioreactors or outdoor ponds. Growth is modeled by first estimating the light attenuation by biomass according to Beer‐Lambert's Law, and then calculating the specific growth rate in discretized culture volume slices that receive declining light intensities due to attenuation. The model uses only two physical and two species‐specific biological input parameters, all of which are relatively easy to determine: incident light intensity, culture depth, as well as the biomass light absorption coefficient and the specific growth rate as a function of light intensity. Roux bottle culture experiments were performed with Nannochloropsis salina at constant temperature (23°C) at six different incident light intensities (10, 25, 50, 100, 250, and 850 µmol/m² s) to determine both the specific growth rate under non‐shading conditions and the biomass light absorption coefficient as a function of light intensity. The model was successful in predicting the biomass growth rate in these Roux bottle batch cultures during the light‐limited linear phase at different incident light intensities. Model predictions were moderately sensitive to minor variations in the values of input parameters. The model was also successful in predicting the growth performance of Chlorella sp. cultured in LED‐lighted 800 L raceway ponds operated in batch mode at constant temperature (30°C) and constant light intensity (1,650 µmol/m² s). Measurements of oxygen concentrations as a function of time demonstrated that following exposure to darkness, it takes at least 5 s for cells to initiate dark respiration. As a result, biomass loss due to dark respiration in the aphotic zone of a culture is unlikely to occur in highly mixed small‐scale photobioreactors where cells move rapidly in and out of the light. By contrast, as supported also by the growth model, biomass loss due to dark respiration occurs in the dark zones of the relatively less well‐mixed pond cultures. In addition to screening novel microalgae strains for high biomass productivities, the model can also be used for optimizing the pond design and operation. Additional research is needed to validate the biomass growth model for other microalgae species and for the more realistic case of fluctuating temperatures and light intensities observed in outdoor pond cultures. Biotechnol. Bioeng. 2013; 110: 1583–1594. © 2012 Wiley Periodicals, Inc.     

Evolving DNA motifs to predict GeneChip probe performance

Background  

Affymetrix High Density Oligonuclotide Arrays (HDONA) simultaneously measure expression of thousands of genes using millions of probes. We use correlations between measurements for the same gene across 6685 human tissue samples from NCBI's GEO database to indicated the quality of individual HG-U133A probes. Low correlation indicates a poor probe.     

Mathematical model to predict drivers' reaction speeds

Mental distractions and physical impairments can increase the risk of accidents by affecting a driver's ability to control the vehicle. In this article, we developed a linear mathematical model that can be used to quantitatively predict drivers' performance over a variety of possible driving conditions. Predictions were not limited only to conditions tested, but also included linear combinations of these tests conditions. Two groups of 12 participants were evaluated using a custom drivers' reaction speed testing device to evaluate the effect of cell phone talking, texting, and a fixed knee brace on the components of drivers' reaction speed. Cognitive reaction time was found to increase by 24% for cell phone talking and 74% for texting. The fixed knee brace increased musculoskeletal reaction time by 24%. These experimental data were used to develop a mathematical model to predict reaction speed for an untested condition, talking on a cell phone with a fixed knee brace. The model was verified by comparing the predicted reaction speed to measured experimental values from an independent test. The model predicted full braking time within 3% of the measured value. Although only a few influential conditions were evaluated, we present a general approach that can be expanded to include other types of distractions, impairments, and environmental conditions.     

An electrodiffusion model for the blood-brain barrier permeability to charged molecules

The endothelial surface glycocalyx layer (SGL) and the basement membrane (BM) are two important components of the blood-brain barrier (BBB). They provide large resistance to solute transport across the BBB in addition to the tight junctions in the cleft between adjacent endothelial cells. Due to their glycosaminoglycan compositions, they carry negative charge under physiological conditions. To investigate the charge effect of the SGL and BM on the BBB permeability to charged solutes, we developed an electrodiffusion model for the transport of charged molecules across the BBB. In this model, constant charge densities were assumed in the SGL and in the BM. Both electrostatic and steric interaction and exclusion to charged molecules were considered within the SGL and the BM and at their interfaces with noncharged regions of the BBB. On the basis of permeability data for the positively charged ribonuclease (+4,radius=2.01?nm) and negatively charged α-lactalbumin (-10,radius=2.08?nm) measured in intact rat mesenteric and pial microvessels, our model predicted that the charge density in both SGL and BM would be ～30?mEq/L, which is comparable to that in the SGL of mesenteric microvessels. Interestingly, our model also revealed that due to the largest concentration drop in the BM, there is a region with a higher concentration of negatively charged α-lactalbumin in the uncharged inter-endothelial cleft, although the concentration of α-lactalbumin is always lower than that of positively charged ribonuclease and that of a neutral solute in the charged SGL and BM.     

The use of Artemia biomass sampling to predict cyst yields in culture ponds

The possibility of using biomass volume (= mean biomass present in the pond.week ^–1) to predict the total amount of harvestable cysts (= kg wet weight collected. week ^–1) produced in a culture pond by an Artemia franciscana population using a mixed model regression was evaluated for two different sampling methods; horizontal transects and vertical point samples. For transects, the following equation was found: `log (0.01 + cyst yields) = –2.05 + 0.025<sup>*</sup>(biomass volume)' with F <sub>(1, 4.87)</sub> = 8.83 and p = 0.032. For the point samples, the regression was also significant with F <sub>(1, 55.2)</sub> = 13.62 and p = 0.0005 for following equation: `log (0.01 + cyst yield) = –3.613 + 0.021^*(biomass volume). As pond effect and interaction terms did not significantly explain a significant portion of the variance for either of the sampling methods (Transects: pond: F _{(3, 14.3)} = 2.48; p = 0.103; pond^*biomass volume: F _{(3, 3.61)} = 4.63; p = 0.0976; Point samples: pond: F _{(3, 44.5)} = 0.00; p = 0.999; pond^*biomass volume: F _{(3, 44.2)} = 0.11; p = 0.954 ), the variable pond (repeated measurement factor) was not included in the final calculations for the regression equations. Although a combination of factors influences the equation, the high significance levels of the regression indicate biomass volume can be safely used to predict production trends. The low investment requirements of this method make it especially attractive for on farm use, where correctly determining the point of cyst decline will help farmers to allocate resources where needed.     

Evaluation method for the drying performance of enzyme containing formulations

A method is presented for fast and cheap evaluation of the performance of enzyme containing formulations in terms of preserving the highest enzyme activity during spray drying. The method is based on modeling the kinetics of the thermal inactivation reaction which occurs during the drying process. Relevant kinetic parameters are determined from differential scanning calorimeter (DSC) experiments and the model is used to simulate the severity of the inactivation reaction for temperatures and moisture levels relevant for spray drying. After conducting experiments and subsequent simulations for a number of different formulations it may be deduced which formulation performs best. This is illustrated by a formulation design study where 4 different enzyme containing formulations are evaluated. The method is validated by comparison to pilot scale spray dryer experiments.     

Simplifying a physiologically structured population model to a stage-structured biomass model

We formulate and analyze an archetypal consumer-resource model in terms of ordinary differential equations that consistently translates individual life history processes, in particular food-dependent growth in body size and stage-specific differences between juveniles and adults in resource use and mortality, to the population level. This stage-structured model is derived as an approximation to a physiologically structured population model, which accounts for a complete size-distribution of the consumer population and which is based on assumptions about the energy budget and size-dependent life history of individual consumers. The approximation ensures that under equilibrium conditions predictions of both models are completely identical. In addition we find that under non-equilibrium conditions the stage-structured model gives rise to dynamics that closely approximate the dynamics exhibited by the size-structured model, as long as adult consumers are superior foragers than juveniles with a higher mass-specific ingestion rate. When the mass-specific intake rate of juvenile consumers is higher, the size-structured model exhibits single-generation cycles, in which a single cohort of consumers dominates population dynamics throughout its life time and the population composition varies over time between a dominance by juveniles and adults, respectively. The stage-structured model does not capture these dynamics because it incorporates a distributed time delay between the birth and maturation of an individual organism in contrast to the size-structured model, in which maturation is a discrete event in individual life history. We investigate model dynamics with both semi-chemostat and logistic resource growth.     

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Background

Osteoarthritis (OA) is the most common disease of arthritis. Analgesics are widely used in the treat of arthritis, which may increase the risk of cardiovascular diseases by 20% to 50% overall.There are few studies on the side effects of OA medication, especially the risk prediction models on side effects of analgesics. In addition, most prediction models do not provide clinically useful interpretable rules to explain the reasoning process behind their predictions. In order to assist OA patients, we use the eXtreme Gradient Boosting (XGBoost) method to balance the accuracy and interpretability of the prediction model.

Results

In this study we used the XGBoost model as a classifier, which is a supervised machine learning method and can predict side effects of analgesics for OA patients and identify high-risk features (RFs) of cardiovascular diseases caused by analgesics. The Electronic Medical Records (EMRs), which were derived from public knee OA studies, were used to train the model. The performance of the XGBoost model is superior to four well-known machine learning algorithms and identifies the risk features from the biomedical literature. In addition the model can provide decision support for using analgesics in OA patients.

Conclusion

Compared with other machine learning methods, we used XGBoost method to predict side effects of analgesics for OA patients from EMRs, and selected the individual informative RFs. The model has good predictability and interpretability, this is valuable for both medical researchers and patients.

     

单木生物量模型估计区域尺度生物量的不确定性

采用系统抽样体系江西省固定样地杉木连续观测数据和生物量数据,通过Monte Carlo法反复模拟由单木生物量模型推算区域尺度地上生物量的过程,估计了江西省杉木地上总生物量。基于不同水平建模样本量n及不同决定系数R~2的设计,分别研究了单木生物量模型参数变异性及模型残差变异性对区域尺度生物量估计不确定性的影响。研究结果表明:2009年江西省杉木地上生物量估计值为(19.84±1.27)t/hm~2,不确定性占生物量估计值约6.41%。生物量估计值和不确定性值达到平稳状态所需的运算时间随建模样本量及决定系数R~2的增大而减小;相对于模型参数变异性,残差变异性对不确定性的影响更小。     

Structure of the skin barrier and its modulation by vesicular formulations

The natural function of the skin is to protect the body from unwanted influences from the environment. The main barrier of the skin is located in the outermost layer of the skin, the stratum corneum. Since the lipids regions in the stratum corneum form the only continuous structure, substances applied onto the skin always have to pass these regions. For this reason the organization in the lipid domains is considered to be very important for the skin barrier function. Due to the exceptional stratum corneum lipid composition, with long chain ceramides, free fatty acids and cholesterol as main lipid classes, the lipid phase behavior is different from that of other biological membranes. In stratum corneum crystalline phases are predominantly present, but most probably a subpopulation of lipids forms a liquid phase. Both the crystalline nature and the presence of a 13 nm lamellar phase are considered to be crucial for the skin barrier function. Since it is impossible to selectively extract individual lipid classes from the stratum corneum, the lipid organization has been studied in vitro using isolated lipid mixtures. These studies revealed that mixtures prepared with isolated stratum corneum lipids mimic to a high extent stratum corneum lipid phase behavior. This indicates that proteins do not play an important role in the stratum corneum lipid phase behavior. Furthermore, it was noticed that mixtures prepared only with ceramides and cholesterol already form the 13 nm lamellar phase. In the presence of free fatty acids the lattice density of the structure increases. In stratum corneum the ceramide fraction consists of various ceramide subclasses and the formation of the 13 nm lamellar phase is also affected by the ceramide composition. Particularly the presence of ceramide 1 is crucial. Based on these findings a molecular model has recently been proposed for the organization of the 13 nm lamellar phase, referred to as "the sandwich model", in which crystalline and liquid domains coexist. The major problem for topical drug delivery is the low diffusion rate of drugs across the stratum corneum. Therefore, several methods have been assessed to increase the permeation rate of drugs temporarily and locally. One of the approaches is the application of drugs in formulations containing vesicles. In order to unravel the mechanisms involved in increasing the drug transport across the skin, information on the effect of vesicles on drug permeation rate, the permeation pathway and perturbations of the skin ultrastructure is of importance. In the second part of this paper the possible interactions between vesicles and skin are described, focusing on differences between the effects of gel-state vesicles, liquid-state vesicles and elastic vesicles.     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement. This paper briefly introduced the original Resonant Recognition Model (RRM), and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM). The key characteristic of the new model is that it can predict directly the protein-protein interaction from the primary sequence, and the MRRM is more suitable than the RRM for this prediction. The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction. Translated from Journal of Shanghai University (Natural Science), 2006, 12(1): 69–73 [译自: 上海大学学报(自然科学版)]     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement.This paper briefly introduced the original Resonant Recognition Model (RRM),and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM).The key characteristic of the new model is that it can predict directly the proteinprotein interaction from the primary sequence,and the MRRM is more suitable than the RRM for this prediction.The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction.     

A model to predict canine pelvic limb musculoskeletal geometry

We developed a model to predict the three-dimensional canine pelvic limb muscular geometry (i.e., all muscle moment arms during any instant in gait). Forty-one muscle origins and insertions, as well as external landmarks (to obtain anthropometric dimensions) were marked on both pelvic limbs of five dogs and digitized on biplanar radiographs. Reference frames in the pelvis, femur, and tibia established the three-dimensional coordinates of each origin, insertion, and landmark. A set of dimensionless 'scaled coordinates' was created by dividing the actual origin and insertion coordinates by selected anthropometric dimensions of each animal. Combining scaled coordinates from all ten limbs produced an averaged 'template' of scaled coordinates. To provide limited validation of the scaling procedure, we measured the anthropometric dimensions between externally palpable landmarks of two additional pelvic limbs. The anthropometric dimensions were multiplied by the averaged template coordinates to calculate two new sets of hindlimb muscle coordinates within the three bony reference frames. The two limbs then were dissected, muscle endpoints were marked, and biplanar radiographs of each of the limb segments were digitized. The actual coordinates so obtained were similar to those predicted by the template and anthropometric measures.     

A provisional model to predict blood pressure response to biofeedback-assisted relaxation

Blood pressure (BP) response to biofeedback-assisted relaxation is not uniform among hypertensive individuals. The purpose of this exploratory study was to determine if selected psychophysiological variables could be used to identify individuals able to lower blood pressure using biofeedback-assisted relaxation. Responders were defined using a preset criterion of 5 mm Hg or greater decrease in mean arterial pressure. A logistic regression model derived from five variables (heart rate, finger temperature, forehead muscle tension, plasma renin response to furosemide, and mean arterial pressure response to furosemide) provided significant predictive power for BP response, exhibiting a sensitivity of 84.6% and a specificity of 80.0%. With future validation, the proposed model may provide useful information to identify patients likely to benefit from biofeedback-assisted relaxation.     

Simple anthropometric and physical performance tests to predict maximal box-lifting ability

Box-lifting ability is an important characteristic of military personnel. The purpose of this paper was to determine the usefulness of the upright row free weight exercise and simple anthropometric tests to predict maximal box-lifting performance that simulates the loading of military supply vehicles. Two groups of adults performed maximal box lifts to 1.4 m (study 1) and 1.7 m (study 2), respectively. All subjects were also tested for upright row 1 repetition maximum (1RM) strength, body mass, height, and body composition. In study 1, a remarkably good prediction of maximal box-lift performance to 1.4 m (42 +/- 12 kg) was obtained from a regression equation including the variables body mass, body composition, and upright row 1RM. Approximately 95% of the variation in 1.4-m box-lifting performance could be accounted for. In contrast, in study 2, only 80% of the variation in 1.7-m box-lifting performance (51 +/- 15 kg) could be accounted for by the best predictor equation. Upright row 1RM strength appears to be a useful tool in the prediction of box-lifting ability to approximately chest height for most adults, probably due to a close match between the muscle groups and contraction modes required during both tasks. Military or other organizations could use the data reported here to substitute simple anthropometry and a 1RM test of strength and for the direct assessment of 1.4-m box-lifting performance.     

Landmark registering waveform data improves the ability to predict performance measures

The purpose of this study was to investigate the benefit of landmark registration when applied to waveform data. We compared the ability of data reduced from time-normalised and landmark registered vertical ground reaction force (vGRF) waveforms captured during maximal countermovement jumps (CMJ) of 53 active male subjects to predict jump height. vGRF waveforms were landmark registered using different landmarks resulting in four registration conditions: (i) end of the eccentric phase, (ii) adding maximum centre of mass (CoM) power, (iii) adding minimum CoM power, (iv) adding minimum vGRF. In addition to the four registration conditions, the non-registered vGRF and concentric phase only were time-normalised and used in subsequent analysis. Analysis of characterising phases was performed to reduce the vGRF data to features that captured the behaviour of each waveform. These features were extracted from each condition’s vGRF waveform, time-domain (time taken to complete the movement), and warping functions (generated from landmark registration). The identified features were used as predictor features to fit a step-wise multilinear regression to jump height. Features generated from the best performing registration condition were able to predict jump height to a similar extent as the concentric phase (86–87%), while all registration conditions could explain jump height to a greater extent than time-normalisation alone (65%). This suggests waveform variability was reduced as phases of the CMJ were aligned. However, findings suggest that over-registration can occur when applying landmark registration. Overall, landmark registration can improve prediction power to performance measures as waveform data can be reduced to more appropriate performance related features.