A high-throughput assay for quantifying phenotypic traits of microalgae

High-throughput methods for phenotyping microalgae are in demand across a variety of research and commercial purposes. Many microalgae can be readily cultivated in multi-well plates for experimental studies which can reduce overall costs, while measuring traits from low volume samples can reduce handling. Here we develop a high-throughput quantitative phenotypic assay (QPA) that can be used to phenotype microalgae grown in multi-well plates. The QPA integrates 10 low-volume, relatively high-throughput trait measurements (growth rate, cell size, granularity, chlorophyll a, neutral lipid content, silicification, reactive oxygen species accumulation, and photophysiology parameters: ETRmax, Ik, and alpha) into one workflow. We demonstrate the utility of the QPA on Thalassiosira spp., a cosmopolitan marine diatom, phenotyping six strains in a standard nutrient rich environment (f/2 media) using the full 10-trait assay. The multivariate phenotypes of strains can be simplified into two dimensions using principal component analysis, generating a trait-scape. We determine that traits show a consistent pattern when grown in small volume compared to more typical large volumes. The QPA can thus be used for quantifying traits across different growth environments without requiring exhaustive large-scale culturing experiments, which facilitates experiments on trait plasticity. We confirm that this assay can be used to phenotype newly isolated diatom strains within 4 weeks of isolation. The QPA described here is highly amenable to customisation for other traits or unicellular taxa and provides a framework for designing high-throughput experiments. This method will have applications in experimental evolution, modelling, and for commercial applications where screening of phytoplankton traits is of high importance

Fracture toughness characterization of syntactic foams

Hollow particle filled polymeric materials called syntactic foams are used as core materials in sandwich composite structures. Syntactic foams find applications in aeronautical and space structures and therefore demand careful study and testing before they can be put to service. In the first part of this thesis work, syntactic foams are fabricated by varying the volume fraction of microballoons and also their density. Four different densities of microballoons are used ranging from 0.22 g/cc to 0.46 g/cc. The volume fraction of the microballoons is varied from 30% to 65%. A set of 3-point bending tests are conducted on these foam samples to determine their fracture toughness. It has been found that fracture toughness decreases with increase in volume fraction of the microballoons. As the microballoon density increases the fracture toughness also increases. From these current and previous studies it is found that the optimum volume fraction of microballoons is around 30%. Scanning Electron Microscopy analysis shows that at low volume fractions of 30% the failure mechanism is primarily due to the formation of micro cracks. These secondary micro cracks provide a toughening mechanism which is the reason for higher fracture toughness at this low volume fraction. As the volume fraction of microballoons increases due to the reduction in inter-particle distance, debonding occurs and the samples fail at much lower loads resulting in low fracture toughness values. In the second part of the study, samples are fabricated by incorporating two types of rubber particles. The volume fraction of the rubber particles is maintained constant at 2% and microballoon volume fraction at 63%. Load deflection curves show some limited plastic deformation just before the specimen fractures. Reinforcing with rubber increases the density by 15% and the fracture toughness by 35%. Rubber reinforcement also improves the crack propagation properties by changing the fracture pattern to the ductile mode. There is strong adhesion between the rubber particles and the matrix material. Micrographs show the rubber particles fractured resulting in an increase of the facture toughness

Pedestrian Mobility Mining with Movement Patterns

In street-based mobility mining, pedestrian volume estimation receives increasing attention, as it provides important applications such as billboard evaluation, attraction ranking and emergency support systems. In practice, empirical measurements are sparse due to budget limitations and constrained mounting options. Therefore, estimation of pedestrian quantity is required to perform pedestrian mobility analysis at unobserved locations. Accurate pedestrian mobility analysis is difficult to achieve due to the non-random path selection of individual pedestrians (resulting from motivated movement behaviour), causing the pedestrian volumes to distribute non-uniformly among the traffic network. Existing approaches (pedestrian simulations and data mining methods) are hard to adjust to sensor measurements or require more expensive input data (e.g. high fidelity floor plans or total number of pedestrians in the site) and are thus unfeasible. In order to achieve a mobility model that encodes pedestrian volumes accurately, we propose two methods under the regression framework which overcome the limitations of existing methods. Namely, these two methods incorporate not just topological information and episodic sensor readings, but also prior knowledge on movement preferences and movement patterns. The first one is based on Least Squares Regression (LSR). The advantage of this method is the easy inclusion of route choice heuristics and robustness towards contradicting measurements. The second method is Gaussian Process Regression (GPR). The advantages of this method are the possibilities to include expert knowledge on pedestrian movement and to estimate the uncertainty in predicting the unknown frequencies. Furthermore the kernel matrix of the pedestrian frequencies returned by the method supports sensor placement decisions. Major benefits of the regression approach are (1) seamless integration of expert data and (2) simple reproduction of sensor measurements. Further advantages are (3) invariance of the results against traffic network homeomorphism and (4) the computational complexity depends not on the number of modeled pedestrians but on the traffic network complexity. We compare our novel approaches to state-of-the-art pedestrian simulation (Generalized Centrifugal Force Model) as well as existing Data Mining methods for traffic volume estimation (Spatial k-Nearest Neighbour) and commonly used graph kernels for the Gaussian Process Regression (Squared Exponential, Regularized Laplacian and Diffusion Kernel) in terms of prediction performance (measured with mean absolute error). Our methods showed significantly lower error rates. Since pattern knowledge is not easy to obtain, we present algorithms for pattern acquisition and analysis from Episodic Movement Data. The proposed analysis of Episodic Movement Data involve spatio-temporal aggregation of visits and flows, cluster analyses and dependency models. For pedestrian mobility data collection we further developed and successfully applied the recently evolved Bluetooth tracking technology. The introduced methods are combined to a system for pedestrian mobility analysis which comprises three layers. The Sensor Layer (1) monitors geo-coded sensor recordings on people’s presence and hands this episodic movement data in as input to the next layer. By use of standardized Open Geographic Consortium (OGC) compliant interfaces for data collection, we support seamless integration of various sensor technologies depending on the application requirements. The Query Layer (2) interacts with the user, who could ask for analyses within a given region and a certain time interval. Results are returned to the user in OGC conform Geography Markup Language (GML) format. The user query triggers the (3) Analysis Layer which utilizes the mobility model for pedestrian volume estimation. The proposed approach is promising for location performance evaluation and attractor identification. Thus, it was successfully applied to numerous industrial applications: Zurich central train station, the zoo of Duisburg (Germany) and a football stadium (Stade des Costières Nîmes, France)

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at various scales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite rainfall estimates could be used as a sole product (e.g., in areas with no (or poor) ground observations) or through integrating with rain gauge measurements. In this study, the potential of a newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall product has been evaluated in comparison to rain gauge data over the Upper Blue Nile basin in Ethiopia for the period of 2000 to 2015. In addition, the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT 3) and the African Rainfall Climatology (ARC 2) products have been used as a benchmark and compared with CHIRPS. From the overall analysis at dekadal (10 days) and monthly temporal scale, CHIRPS exhibited better performance in comparison to TAMSAT 3 and ARC 2 products. An evaluation based on categorical/volumetric and continuous statistics indicated that CHIRPS has the greatest skills in detecting rainfall events (POD D0.99, 1.00) and measure of volumetric rainfall (VHID1.00, 1.00), the highest correlation coefficients (r D0.81, 0.88), better bias values (0.96, 0.96), and the lowest RMSE (28.45mmdekad 1, 59.03mmmonth 1) than TAMSAT 3 and ARC 2 products at dekadal and monthly analysis, respectively. CHIRPS overestimates the frequency of rainfall occurrence (up to 31% at dekadal scale), although the volume of rainfall recorded during those events was very small. Indeed, TAMSAT 3 has shown a comparable performance with that of the CHIRPS product, mainly with regard to bias. The ARC 2 product was found to have the weakest performance underestimating rain gauge observed rainfall by about 24 %. In addition, the skill of CHIRPS is less affected by variation in elevation in comparison to TAMSAT 3 and ARC 2 products. CHIRPS resulted in average biases of 1.11, 0.99, and 1.00 at lower (\u3c 1000ma.s.l.), medium (1000 to 2000ma.s.l.), and higher elevation (\u3e 2000ma.s.l.), respectively. Overall, the finding of this validation study shows the potentials of the CHIRPS product to be used for various operational applications such as rainfall pattern and

Applications of Forensic Evidence in Criminal Cases

In 2003, Massachusetts governor Mitt Romney proposed a plan for an infallible death penalty that required irrefutable scientific evidence, effectively removing any doubt regarding potential innocence in death penalty cases. Forensic science encompasses many scientific disciplines including natural sciences and pattern analysis, but not all such areas experience equal amounts of general acceptance or influence in criminal cases. While DNA analysis and fingerprint identification using the Integrated Automated Fingerprint Identification System (IAFIS) are both widely accepted forensic applications, recent events expose concerns regarding the authenticity of other disciplines such as hair and bite mark comparison. Before policymakers address the issue of a reinstated death penalty, they must carefully consider the merits of forensic science as well as the potential dangers. Existing issues and a history of wrongful convictions aided by flawed forensic testimony necessitate further investigation and critical analysis of forensic disciplines and the application of forensic evidence in criminal cases