Implementing Eco-Friendly Housing Techniques in Western Montana: Green Home Montana: Eco-friendly Housing and Living Practices - Final Capstone Portfolio

While the green building movement is common practice in the commercial realm, it is not yet widely popular with residential buildings. We considered the question “How can residents of western Montana adopt eco-friendly housing practices that are locally appropriate and relevant?” There is an opportunity to increase green living practices among renters and homeowners in western Montana through retrofitting, gardening, composting, and similar behaviors. By considering climatic factors relevant to the region, suggestions for relevant eco-friendly practices can be made available to homeowners and renters alike. We will research green living practices used in other countries with similar climatic factors as western Montana. We will then make a website to help streamline locally relevant information catered to help residents take action towards their sustainability goals. We will survey a sample of residents throughout western Montana to inform the materials provided on the website. For example, these materials may include, but are not limited to, sustainability project demonstration videos, links to local builders, history, blogs, global initiatives, and links to other resources. We expect this website to be relevant and increase accessibility to western Montana renters and homeowners

UC-39 Journalistic Integrity vis Artifical Intelligence

We are developing a web app to recognize and rate political bias in online journalism using artificial intelligence. All human writing inherently contains bias ,however bias is less harmful if it is transparent to the reader because they can now make informed decisions about what they read. We\u27ve collected articles and and reactions to them from online sources, and then used Neural Networks trained for natural language processing to determine bias. The project can predict bias labels on a news articles with 82% accuracy.Advisors(s): Reza Meimandi Parizi - course instructor Asher Nuckolls - project ownerTopic(s): Artificial IntelligenceSWE472

Three-dimensional trajectories and network analyses of group behaviour within chimney swift flocks during approaches to the roost

Chimney swifts (Chaetura pelagica) are highly manoeuvrable birds notable for roosting overnight in chimneys, in groups of hundreds or thousands of birds, before and during their autumn migration. At dusk, birds gather in large numbers from surrounding areas near a roost site. The whole flock then employs an orderly, but dynamic, circling approach pattern before rapidly entering a small aperture en masse. We recorded the three-dimensional trajectories of ≈1 800 individual birds during a 30 min period encompassing flock formation, circling, and landing, and used these trajectories to test several hypotheses relating to flock or group behaviour. Specifically, we investigated whether the swifts use local interaction rules based on topological distance (e.g. the n nearest neighbours, regardless of their distance) rather than physical distance (e.g. neighbours within x m, regardless of number) to guide interactions, whether the chimney entry zone is more or less cooperative than the surrounding flock, and whether the characteristic subgroup size is constant or varies with flock density. We found that the swift flock is structured around local rules based on physical distance, that subgroup size increases with density, and that there exist regions of the flock that are less cooperative than others, in particular the chimney entry zone

The relationship between cognitive ability and BOLD activation across sleep–wake states

The sleep spindle, a waxing and waning oscillation in the sigma frequency range, has been shown to correlate with fluid intelligence; i.e. the ability to use logic, learn novel rules/patterns, and solve problems. Using simultaneous EEG and fMRI, we previously identified the neural correlates of this relationship, including activation of the thalamus, bilateral putamen, medial frontal gyrus, middle cingulate cortex, and precuneus. However, research to date has focussed primarily on non-rapid eye movement (NREM) sleep, and spindles per se, thus overlooking the possibility that brain activity that occurs in other sleep–wake states might also be related to cognitive abilities. In our current study, we sought to investigate whether brain activity across sleep/wake states is also related to human intelligence in N = 29 participants. During NREM sleep, positive correlations were observed between fluid intelligence and blood oxygen level dependent (BOLD) activations in the bilateral putamen and the paracentral lobule/precuneus, as well as between short-term memory (STM) abilities and activity in the medial frontal cortex and inferior frontal gyrus. During wake, activity in bilateral postcentral gyri and occipital lobe was positively correlated with short-term memory abilities. In participants who experienced REM sleep in the scanner, fluid intelligence was positively associated with midbrain activation, and verbal intelligence was associated with right postcentral gyrus activation. These findings provide evidence that the relationship between sleep and intellectual abilities exists beyond sleep spindles

HDAC 1 and 6 modulate cell invasion and migration in clear cell renal cell carcinoma

Indexación: Web of ScienceBackground: Class I histone deacetylases (HDACs) have been reported to be overexpressed in clear cell renal cell carcinoma (ccRCC), whereas the expression of class II HDACs is unknown. Methods: Four isogenic cell lines C2/C2VHL and 786-O/786-OVHL with differential VHL expression are used in our studies. Cobalt chloride is used to mimic hypoxia in vitro. HIF-2 alpha knockdowns in C2 and 786-O cells is used to evaluate the effect on HDAC 1 expression and activity. Invasion and migration assays are used to investigate the role of HDAC 1 and HDAC 6 expression in ccRCC cells. Comparisons are made between experimental groups using the paired T-test, the two-sample Student's T-test or one-way ANOVA, as appropriate. ccRCC and the TCGA dataset are used to observe the clinical correlation between HDAC 1 and HDAC 6 overexpression and overall and progression free survival. Results: Our analysis of tumor and matched non-tumor tissues from radical nephrectomies showed overexpression of class I and II HDACs (HDAC6 only in a subset of patients). In vitro, both HDAC1 and HDAC6 over-expression increased cell invasion and motility, respectively, in ccRCC cells. HDAC1 regulated invasiveness by increasing matrix metalloproteinase (MMP) expression. Furthermore, hypoxia stimulation in VHL-reconstituted cell lines increased HIF isoforms and HDAC1 expression. Presence of hypoxia response elements in the HDAC1 promoter along with chromatin immunoprecipitation data suggests that HIF-2 alpha is a transcriptional regulator of HDAC1 gene. Conversely, HDAC6 and estrogen receptor alpha (ER alpha) were co-localized in cytoplasm of ccRCC cells and HDAC6 enhanced cell motility by decreasing acetylated alpha-tubulin expression, and this biological effect was attenuated by either biochemical or pharmacological inhibition. Finally, analysis of human ccRCC specimens revealed positive correlation between HIF isoforms and HDAC. HDAC1 mRNA upregulation was associated with worse overall survival in the TCGA dataset. Conclusions: Taking together, these results suggest that HDAC1 and HDAC6 may play a role in ccRCC biology and could represent rational therapeutic targets.http://bmccancer.biomedcentral.com/articles/10.1186/s12885-016-2604-

Positive and Negative Emotions Predict Weight Loss Intentions and Behaviors beyond Theory of Planned Behavior Constructs

Acknowledgements: This study was funded by the ScienceCampus Tuebingen (TP7.1) awarded to Devin G. Ray and Kai Sassenberg.Peer reviewedPostprin

IntroUNET: Identifying introgressed alleles via semantic segmentation

A growing body of evidence suggests that gene flow between closely related species is a widespread phenomenon. Alleles that introgress from one species into a close relative are typically neutral or deleterious, but sometimes confer a significant fitness advantage. Given the potential relevance to speciation and adaptation, numerous methods have therefore been devised to identify regions of the genome that have experienced introgression. Recently, supervised machine learning approaches have been shown to be highly effective for detecting introgression. One especially promising approach is to treat population genetic inference as an image classification problem, and feed an image representation of a population genetic alignment as input to a deep neural network that distinguishes among evolutionary models (i.e. introgression or no introgression). However, if we wish to investigate the full extent and fitness effects of introgression, merely identifying genomic regions in a population genetic alignment that harbor introgressed loci is insufficient—ideally we would be able to infer precisely which individuals have introgressed material and at which positions in the genome. Here we adapt a deep learning algorithm for semantic segmentation, the task of correctly identifying the type of object to which each individual pixel in an image belongs, to the task of identifying introgressed alleles. Our trained neural network is thus able to infer, for each individual in a two-population alignment, which of those individual’s alleles were introgressed from the other population. We use simulated data to show that this approach is highly accurate, and that it can be readily extended to identify alleles that are introgressed from an unsampled “ghost” population, performing comparably to a supervised learning method tailored specifically to that task. Finally, we apply this method to data from Drosophila, showing that it is able to accurately recover introgressed haplotypes from real data. This analysis reveals that introgressed alleles are typically confined to lower frequencies within genic regions, suggestive of purifying selection, but are found at much higher frequencies in a region previously shown to be affected by adaptive introgression. Our method’s success in recovering introgressed haplotypes in challenging real-world scenarios underscores the utility of deep learning approaches for making richer evolutionary inferences from genomic data

Los mártires de la andadura del pueblo

Bello poema dedicado al martirio de Monseñor Oscar Arnulfo Romero y Galdámez por Monseñor Pedro Casaldáliga, conocido este último como el “obispo de los pobres” y ex Obispo de, São Félix do Araguaia, Brasil

3D for the people: multi-camera motion capture in the field with consumer-grade cameras and open source software

Ecological, behavioral and biomechanical studies often need to quantify animal movement and behavior in three dimensions. In laboratory studies, a common tool to accomplish these measurements is the use of multiple, calibrated high-speed cameras. Until very recently, the complexity, weight and cost of such cameras have made their deployment in field situations risky; furthermore, such cameras are not affordable to many researchers. Here, we show how inexpensive, consumer-grade cameras can adequately accomplish these measurements both within the laboratory and in the field. Combined with our methods and open source software, the availability of inexpensive, portable and rugged cameras will open up new areas of biological study by providing precise 3D tracking and quantification of animal and human movement to researchers in a wide variety of field and laboratory contexts