As dam removal becomes more accepted as an effective approach to river restoration, understanding the upstream channel geomorphic response is vital. This study is being conducted to examine upstream channel evolution of the Blackfoot River (BFR) in response to an 8-meter drop in base level that was caused by the 2008 removal of the Milltown Dam. This research is testing the hypothesis that geomorphic response will be more pronounced closest to the site of the dam with more incision occurring on the bed and this response decreasing upstream. For this study, longitudinal, cross section elevation and grain size data from 13 BFR sites will be collected: seven cross sections within the area influenced by Milltown Reservoir and six cross sections upstream. To quantify net geomorphic change, two variables are being investigated: (1) the change in grain size and, (2) the adjustment of channel geometry over a three-year period (2008-2011). Preliminary observations indicate that in addition to distance from the dam other geomorphic factors such as grain size and channel confinement also drive channel response. Long-term studies of upstream channel response to dam removals are rare. Results from this study will help increase understanding of this dam removal and can be applied to future dam remediation projects.

Through investigating the audio features of sounds and different machine learning algorithms, we aim to develop a computational framework that automatically identifies and extracts desired sounds from audio clips of the Blackfeet language. The data acquired from this framework will be used to compile a database that will facilitate the digital preservation of the language. Many machine learning algorithms require training data to learn from, and test data to apply that knowledge on. The first step of this project was to create training data by manually identifying occurrences of a desired sound and associating them with sets of quantitative sound features. The next step is to identify a set of audio features that best characterizes the desired sound. This is accomplished through understanding and applying related research results, manual analysis of the training data, and trial and error. The quality of characterization is measured by the percentage of correctly characterized sounds, given a set of audio features and a learning algorithm. This is the first computational linguistic system applied to the Blackfeet language. If successful, similar systems can be implemented for other indigenous languages. Blackfeet is a local Montanan, Native American language that is critically endangered with only 5000 speakers in Canada and 100 in US, most of whom are elderly. Therefore, it is vitally important to preserve this language.

PURPOSE: Acetylcholine (ACh) is a neurotransmitter in central and peripheral nervous systems involved in learning, memory, and movement. Previous studies have shown abnormal phenotypes, such as hyperactivity and cognitive deficits, in mice lacking specific types of acetylcholine receptors. Because of the diverse distribution of acetylcholine receptors, our ability to investigate specific neuronal pathways has been limited. One approach is to target a specific subset of cells expressing acetylcholine receptors through transgenic recombination. Using this approach, we have successfully generated a mouse line, the PV-CRE/FM1 mouse, which is deficient in M1 acetylcholine receptors on parvalbumin (PV+) positive cells, which will allow for specific investigation of acetylcholine functioning in this key subset of inhibitory cells.

METHOD: Utilizing state-of-the-art animal tracking software, abnormal hyperactivity and/or cognitive deficits, if present, can be detected in homozygous PV-CRE/FM1 mice compared to a wild type control group.

ORIGINALITY: Global M1 knockout mice have shown a number of abnormal phenotypes, including hyperactivity and cognitive deficits. With transgenic recombination, M1 receptors can specifically be eliminated from PV+ inhibitory cells, which will allow the examination of specific M1 mAChR and their effects across specific neuronal pathways.

SIGNIFICANCE: Understanding the neurological pathways involved is essential in targeting and treating neurological disorders such as Parkinson’s disease, Alzheimer’s disease, and epilepsy. Examining the PV-CRE/FM1 mice may allow for another puzzle piece to be added to the complicated pathways of the cholinergic system and bring forth innovative treatments to be implemented in patients suffering from these devastating neurological diseases.

Climate and altitude are the primary drivers in the distribution of snow facies, ice facies and zones on the surface of the Greenland Ice Sheet. There are three different facies: ablation, percolation, and dry-snow, and two zones, lake and dirty ice. Delineating changes in the distribution of different facies and zones on glaciers through time is critical to understanding ice sheet surface processes. I seek to understand and delineate the distribution of different facies and zones on the glacier Isunnguata Sermia in Greenland for the months of May through September in 2010. To delineate the different zones and facies, I used daily Moderate Resolution Imaging Spectroradiometer (MODIS) images for the months of May through September. I downloaded bands 1-6 at 500 meter resolution, reprojected and enhanced each image by running a principle components analysis and creating RGB color composite for each day. I found that the zones moved to higher altitudes later in the summer, with August producing the overall largest change. To investigate the ice below the surface, I used video of thirteen boreholes from June and July 2010. Within the ice, there are two zones: clean ice, and debris-laden ice near the bed. I classified the ice based on clarity of the ice, and the size and amount of debris contained within it. I found that the debris-laden ice was only found within 2 meters of the bed, none was found at the surface in the study area.

Recently, sol-gel chemistry and the use of organic-inorganic hybrid materials have received much attention for use in the remediation of aqueous streams contaminated with heavy metals. Heavy metals are present in local streams from a multitude of sources and have very harmful effects on biological processes. Remediation of these streams is necessary to ensure the health of the ecosystem. There are many different methods for remediation of contaminated waters, but many are either too expensive or have insufficient selectivity for metal ions for them to be cost-effective. Silica polyamine composites (SPC) made via the sol-gel process have been investigated recently because of their high selectivity for specific metal ions. The SPC starting material is synthesized from a mixture of tetramethoxysilane (TMOS), methyltrimethoxysilane (MTMOS) and chloropropyltrimethoxysilane (CPTMOS). This material is converted to the SPC BP-1 by a reaction with the polymer polyallylamine (PAA). When BP-1 is further modified with various organic ligands, the resulting composites show high affinities for specific metal ions and have shown to selectively recover valuable metal ions up to 98% pure. Recently, batch capacities and breakthrough testing have been conducted to determine the metal capacity and the selectivity of the sol-gel composites. The composites made via the sol-gel process have comparable, and sometimes better, capacities and selectivity than commercial composites. Also, the sol-gel process is more environmentally benign than the current methods for the production of SPC and with comparable capacities the sol-gel process can be a viable candidate for the future production of SPC.

Energy consumption was studied in a student computer lab for the purpose of mapping out and reducing the carbon footprint created by the daily use of student lab computers. Data was collected by using four passive energy monitors to record the consumption of fifteen lab computers over a period of three weeks. The accumulated power consumption was recorded after each day of use and each week was used to measure energy consumption with a different set of power management options. These three sets consisted of using first, no power saving options, second, the default power saving options put in place by the University and third, adding increased power saving options inspired by Energy Star. This third set of options was implemented on each computer via a group policy. By using a group policy, each computer was able to automatically change its behavior depending upon whether it was in use by a student or sitting idle. When in use, a computer would only turn off the monitor and hard disk drive after ten minutes of inactivity. Once a student logs off, the computer would enter a sleep state after one minute and consume minimal amounts of energy. After the data collection was complete, it was discovered that an energy savings of approximately 37.7% was attained from the computers. This data can be used to map a carbon footprint for the Campus and aid in helping the University reach its commitment of carbon neutrality by the year 2020.