Experimental and numerical investigation of capillary flow in SU8 and PDMS microchannels with integrated pillars

Microfluidic channels with integrated pillars are fabricated on SU8 and PDMS substrates to understand the capillary flow. Microscope in conjunction with highspeed camera is used to capture the meniscus front movement through these channels for ethanol and isopropyl alcohol, respectively. In parallel, numerical simulations are conducted, using volume of fluid method, to predict the capillary flow through the microchannels with different pillar diameter to height ratio, ranging from 2.19 to 8.75 and pillar diameter to pitch ratio, ranging from 1.44 to 2.6. The pillar size (diameter, pitch and height) and the physical properties of the fluid (surface tension and viscosity) are found to have significant influence on the capillary phenomena in the microchannel. The meniscus displacement is non-uniform due to the presence of pillars and the non-uniformity in meniscus displacement is observed to increase with decrease in pitch to diameter ratio. The surface area to volume ratio is observed to play major roles in the velocity of the capillary meniscus of the devices. The filling speed is observed to change more dramatically under different pillar heights upto 120 mu m and the change is slow with further increase in the pillar height. The details pertaining to the fluid distribution (meniscus front shapes) are obtained from the numerical results as well as from experiments. Numerical predictions for meniscus front shapes agree well with the experimental observations for both SU8 and PDMS microchannels. It is observed that the filling time obtained experimentally matches very well with the simulated filling time. The presence of pillars creates uniform meniscus front in the microchannel for both ethanol and isopropyl alcohol. Generalized plots in terms of dimensionless variables are also presented to predict the performance parameters for the design of these microfluidic devices. The flow is observed to have a very low Capillary number, which signifies the relative importance of surface tension to viscous effects in the present study

Acquired Phototrophy and Its Implications for Bloom Dynamics of the Teleaulax-Mesodinium-Dinophysis-Complex

The dinoflagellate Dinophysis is responsible for causing diarrhetic shellfish poisoning impacting shellfish aquaculture globally. Dinophysis species are invariably plastidic specialist non-constitutive mixoplankton (pSNCM), combining phagotrophy with acquired phototrophy. Dinophysis acquires phototrophy from another pSNCM, the ciliate Mesodinium, which in turn acquires phototrophy from cryptophytes within the Teleaulax-Plagioselmis-Geminigera clade. Despite this trophic linkage, the temporal dynamics of cryptophyte-Mesodinium-Dinophysis remain poorly understood. In this study, we present the first Teleaulax-Mesodinium-Dinophysis (TMD)-complex system dynamics model. Using this, we explored the dynamics of TMD interactions under different ecological settings. Temperature, nutrient load, mixed layer depth, and irradiance all greatly influenced the timing and magnitude of the TMD-complex interactions and, as a result, Dinophysis bloom duration and peak. Availability of Mesodinium and temporal matching of its growth to that of Dinophysis are also key biotic factors; the timing of Mesodinium availability impacts the potential of Dinophysis growth for up to 3 months. Integrating our TMD-complex model with a suitable hydrodynamic model could greatly improve our understanding of bloom formation and aid in forecasting harmful algal bloom (HAB) events. Future monitoring of Dinophysis would also be enhanced by the monitoring of the precursor prey species, Teleaulax and Mesodinium, which are rarely accorded the same effort as the HAB forming dinoflagellate

Comparing the spatio-temporal variability of remotely sensed oceanographic parameters between the Arabian Sea and Bay of Bengal throughout a decade

The spatio-temporal variability of sea-surface temperature (SST), photosynthetically active radiation (PAR), chlorophyll-a (Chl-a), particulate organic carbon (POC) and particulate inorganic carbon (PIC) was evaluated in the Arabian Sea (ABS) and Bay of Bengal (BoB), from July 2002 to November 2014 by means of remotely sensed monthly composite Aqua MODIS level-3 data having a spatial resolution of 4.63 km. Throughout the time period under consideration, the surface waters of ABS (27.76 ± 1.12°C) were slightly cooler than BoB (28.93 ± 0.76°C); this was observed during all the seasons. On the contrary, the availability of PAR was higher in ABS (45.76 ± 3.41 mol m-2 d-1) compared to BoB (41.75 ± 3.75 mol m-2 d-1), and its spatial dynamics in the two basins was mainly regulated by cloud cover and turbidity of the water column. The magnitude and variability of Chl-a concentration were substantially higher in ABS (0.487 ± 0.984 mg m-3), compared to BoB (0.187 ± 0.243 mg m-3), and spatially higher values were observed near the coastal waters. Both POC and PIC exhibited higher magnitudes in ABS compared to BoB; however, the difference was substantially high in case of POC. None of the parameters showed any significant temporal trend during the 12-year span, except PIC, which exhibited a significant decreasing trend in ABS

Control of human endometrial stromal cell motility by PDGF-BB, HB-EGF and trophoblast-secreted factors

Human implantation involves extensive tissue remodeling at the fetal-maternal interface. It is becoming increasingly evident that not only trophoblast, but also decidualizing endometrial stromal cells are inherently motile and invasive, and likely contribute to the highly dynamic processes at the implantation site. The present study was undertaken to further characterize the mechanisms involved in the regulation of endometrial stromal cell motility and to identify trophoblast-derived factors that modulate migration. Among local growth factors known to be present at the time of implantation, heparin-binding epidermal growth factor-like growth factor (HB-EGF) triggered chemotaxis (directed locomotion), whereas platelet-derived growth factor (PDGF)-BB elicited both chemotaxis and chemokinesis (non-directed locomotion) of endometrial stromal cells. Supernatants of the trophoblast cell line AC-1M88 and of first trimester villous explant cultures stimulated chemotaxis but not chemokinesis. Proteome profiling for cytokines and angiogenesis factors revealed neither PDGF-BB nor HB-EGF in conditioned media from trophoblast cells or villous explants, while placental growth factor, vascular endothelial growth factor and PDGF-AA were identified as prominent secretory products. Among these, only PDGF-AA triggered endometrial stromal cell chemotaxis. Neutralization of PDGF-AA in trophoblast conditioned media, however, did not diminish chemoattractant activity, suggesting the presence of additional trophoblast-derived chemotactic factors. Pathway inhibitor studies revealed ERK1/2, PI3 kinase/Akt and p38 signaling as relevant for chemotactic motility, whereas chemokinesis depended primarily on PI3 kinase/Akt activation. Both chemotaxis and chemokinesis were stimulated upon inhibition of Rho-associated, coiled-coil containing protein kinase. The chemotactic response to trophoblast secretions was not blunted by inhibition of isolated signaling cascades, indicating activation of overlapping pathways in trophoblast-endometrial communication. In conclusion, trophoblast signals attract endometrial stromal cells, while PDGF-BB and HB-EGF, although not identified as trophoblast-derived, are local growth factors that may serve to fine-tune directed and non-directed migration at the implantation site

The Isoelectric Region of Proteins: A Systematic Analysis

Background: Binding of proteins in ion exchange chromatography is dominated by electrostatic interactions and can be tuned by adjusting pH and ionic strength of the solvent. Therefore, the isoelectric region (IER), the pH region of almost zero charge near the pI, has been used to predict the binding properties of proteins. Principal findings: Usually the IER is small and binding and elution is carried out at pH values near to the pI. However, some proteins with an extended IER have been shown to bind and elute far away from its pI. To analyze factors that mediate the size of the IER and to identify proteins with an extended IER, two protein families consisting of more than 7000 proteins were systematically investigated. Most proteins were found to have a small IER and thus are expected to bind or elute near to their pI, while only a small fraction of less than 2 % had a large IER. Conclusions: Only four factors, the number of histidines, the pI, the number of titratable amino acids and the ratio of acidic to basic residues, are sufficient to reliably classify proteins by their IER based on their sequence only, and thus to predict their binding and elution behaviour in ion exchange chromatography

Locating previously unknown patterns in data-mining results: a dual data- and knowledge-mining method

BACKGROUND: Data mining can be utilized to automate analysis of substantial amounts of data produced in many organizations. However, data mining produces large numbers of rules and patterns, many of which are not useful. Existing methods for pruning uninteresting patterns have only begun to automate the knowledge acquisition step (which is required for subjective measures of interestingness), hence leaving a serious bottleneck. In this paper we propose a method for automatically acquiring knowledge to shorten the pattern list by locating the novel and interesting ones. METHODS: The dual-mining method is based on automatically comparing the strength of patterns mined from a database with the strength of equivalent patterns mined from a relevant knowledgebase. When these two estimates of pattern strength do not match, a high "surprise score" is assigned to the pattern, identifying the pattern as potentially interesting. The surprise score captures the degree of novelty or interestingness of the mined pattern. In addition, we show how to compute p values for each surprise score, thus filtering out noise and attaching statistical significance. RESULTS: We have implemented the dual-mining method using scripts written in Perl and R. We applied the method to a large patient database and a biomedical literature citation knowledgebase. The system estimated association scores for 50,000 patterns, composed of disease entities and lab results, by querying the database and the knowledgebase. It then computed the surprise scores by comparing the pairs of association scores. Finally, the system estimated statistical significance of the scores. CONCLUSION: The dual-mining method eliminates more than 90% of patterns with strong associations, thus identifying them as uninteresting. We found that the pruning of patterns using the surprise score matched the biomedical evidence in the 100 cases that were examined by hand. The method automates the acquisition of knowledge, thus reducing dependence on the knowledge elicited from human expert, which is usually a rate-limiting step