Microheated substrates for patterning cells and controlling development

Here, we seek to control cellular development by devising a means through which cells can be subjected to a microheated environment in standard culture conditions. Numerous techniques have been devised for controlling cellular function and development via manipulation of surface environmental cues at the micro- and nanoscale. It is well understood that temperature plays a significant role in the rate of cellular activities, migratory behavior (thermotaxis), and in some cases, protein expression. Yet, the effects and possible utilization of micrometer-scale temperature fields in cell cultures have not been explored. Toward this end, two types of thermally isolated microheated substrates were designed and fabricated, one with standard backside etching beneath a dielectric film and another with a combination of surface and bulk micromachining and backside etching. The substrates were characterized with infrared microscopy, finite element modeling, scanning electron microscopy, stylus profilometry, and electrothermal calibrations. Neuron culture studies were conducted on these substrates to 1) examine the feasibility of using a microheated environment to achieve patterned cell growth and 2) selectively accelerate neural development on regions less than 100$mu m$wide. Results show that attached neurons, grown on microheated regions set at 37$~^circ C$, extended processes substantially faster than those incubated at 25$~^circ C$on the same substrate. Further, unattached neurons were positioned precisely along the length of the heater filament (operating at 45$~^circ C$) using free convection currents. These preliminary findings indicate that microheated substrates may be used to direct cellular development spatially in a practical manner.$hfillhbox[1414]

A hierarchical climatic zoning method for energy efficient building design applied in the region with diverse climate characteristics

The climate-responsive strategies for energy efficient building design and management require a detailed understanding of the local climatic conditions, while climate zones are fundamental to building regulations and the application of technologies. Smaller and more homogeneous climate zones could help policy-makers and building designers to improve building energy efficiency while improving the indoor thermal environment. A new climate zoning method, with two-tier classification designed for passive building design, is proposed, using climate data (degree-days, relative humidity, solar radiation and wind speed) with Hierarchical Agglomerative Clustering (HAC) following the Ward’s method. The method is applied to the Hot Summer and Cold Winter (HSCW) zone of China as a showcase, where there are no fine climate zones for energy efficient building design with diverse climate characteristics. Seven sub-zones that consider both cooling and heating demands are generated in Tier 1. In the second tier, the HSCW zone is further sub-divided into three humidity groups, three solar radiation clusters, and four wind speed clusters. To assess the impact of climate zoning on building heating and cooling, EnergyPlus simulations are conducted with the output of heating and cooling load. The cooling loads decrease from sub-zone A to B to C (mean = 82.8, 65.3, 43.8 kWh m-2, respectively) with sub-zone mean heating A1 larger than A2 and A3, B1 larger than B2, and C1 larger than C2, which is in accordance with the assumption made in the first-tier division. The higher wind speeds can raise the possibility of natural ventilation, and further increase the free-running period (FRP) when heating and cooling are not needed. The proposed zones are mapped and provide a useful reference for the policy/building code makers for heating and cooling strategies in this region. The method to create the climate zones could be applied in any region with local climate data

Gentrification and Air Quality in a Large Urban County in the United States

Introduction: Increases in industrialization and manufacturing have led to worsening pollution in some components of air quality. In addition, gentrification is occurring in large cities throughout the world. As these socioeconomic and demographic changes occur, there have been no studies examining the association of gentrification with air quality. To investigate this association, we studied the trends of gentrification, changes in racial distribution and changes in air quality in each zip code of a large urban county over a 40-year period. Methods: We conducted a retrospective longitudinal study over 40 years in Wayne County, Michigan using socioeconomic and demographic data from the National Historical Geographic Information System (NHGIS) and air quality data from the United States Environmental Protection Agency (EPA). To assess gentrification, longitudinal analyses were performed to examine median household income, percentage with a college education, median housing value, median gross rent and employment level. The racial distribution was evaluated in each zip code during the time period. Gentrification was studied in relation to air quality using nonparametric 2-sample Wilcon-Mann-Whitney tests and Binomial Generalized Linear Regression models. Results: Although air quality improved overall over the 40-year period, there was a lesser rate of improvement in gentrified areas. Furthermore, gentrification was strongly associated with racial distribution. The most substantial gentrification occurred from 2010 to 2020, in which a specific cluster of adjacent zip codes in downtown Detroit experienced intense gentrification and a drop in the percentage of African-American residents. Conclusions: Gentrified areas seem to have a less pronounced improvement in air quality over time. This reduction in air quality improvement is likely associated with demolitions and the construction of new buildings, such as sporting arenas and accompanying traffic density. Gentrification is also strongly associated with an increase in non-minority residents in an area. Although previous definitions of gentrification in the literature have not included racial distribution, we suggest that future definitions should include this metric given the strong association. Minority residents who are displaced as a result of gentrification do not experience the improvements in housing quality, accessibility to healthy foods and other associations of gentrification

Film extrusion of sunflower protein isolate

Film extrusion of sunflower protein isolate (SFPI) was studied. The influence of die temperature (85 to 160°C), water and glycerol contents were investigated through appearance, mechanical and thermo-mechanical properties and swelling behavior in water of films. It was demonstrated that highest temperature, well above SFPI denaturation temperature in the compound, highest glycerol content (70 parts for 100 parts of SFPI) and medium water content (20 parts for 100 parts of SFPI) gave the most regular and smoothest film (as seen on SEM micrographs). Its ultimate tensile strength, Young’s modulus and strain at break were respectively: 3.2 MPa, 17.7 MPa and 73%. Soaked in water, its swelling was about 186% w/w but the film was quiet insoluble. Effect of temperature and plasticizer content were discussed in relation to the kinetic of SFPI denaturation. These first results are very promising for the development of biodegradable protein-based films

Sequencing genes in silico using single nucleotide polymorphisms

<p>Abstract</p> <p>Background</p> <p>The advent of high throughput sequencing technology has enabled the 1000 Genomes Project Pilot 3 to generate complete sequence data for more than 906 genes and 8,140 exons representing 697 subjects. The 1000 Genomes database provides a critical opportunity for further interpreting disease associations with single nucleotide polymorphisms (SNPs) discovered from genetic association studies. Currently, direct sequencing of candidate genes or regions on a large number of subjects remains both cost- and time-prohibitive.</p> <p>Results</p> <p>To accelerate the translation from discovery to functional studies, we propose an in silico gene sequencing method (ISS), which predicts phased sequences of intragenic regions, using SNPs. The key underlying idea of our method is to infer diploid sequences (a pair of phased sequences/alleles) at every functional locus utilizing the deep sequencing data from the 1000 Genomes Project and SNP data from the HapMap Project, and to build prediction models using flanking SNPs. Using this method, we have developed a database of prediction models for 611 known genes. Sequence prediction accuracy for these genes is 96.26% on average (ranges 79%-100%). This database of prediction models can be enhanced and scaled up to include new genes as the 1000 Genomes Project sequences additional genes on additional individuals. Applying our predictive model for the KCNJ11 gene to the Wellcome Trust Case Control Consortium (WTCCC) Type 2 diabetes cohort, we demonstrate how the prediction of phased sequences inferred from GWAS SNP genotype data can be used to facilitate interpretation and identify a probable functional mechanism such as protein changes.</p> <p>Conclusions</p> <p>Prior to the general availability of routine sequencing of all subjects, the ISS method proposed here provides a time- and cost-effective approach to broadening the characterization of disease associated SNPs and regions, and facilitating the prioritization of candidate genes for more detailed functional and mechanistic studies.</p