Phosphate Contaminant Detection in Water Through a Paper-based Microfluidic Device

This report describes a project aimed at developing a low-cost, portable, on-site, user-friendly system for detecting different concentrations of phosphate in drinking water. Phosphate is a natural chemical, but toxic in large concentrations; detection is therefore important to avoid drinking contaminated water. Despite this fact, no cheap, and/or nontoxic system for phosphate detection is yet on the market. The detection system utilizes a paper-based microfluidic device to automate the electrochemical detection process, which normally requires expert use of lab equipment. When combined with a portable potentiostat that works with a mobile app, the device will allow untrained users to determine if any source of drinking water contains unsafe levels of phosphate without equipment or training, and to communicate that information to a central database for further analysis. Those of any background, particularly in developing countries, will be able to maintain health and raise awareness about clean water. Microfluidic devices are useful tools for the detection of water contaminants, but there is a gap in technology for the detection of phosphate. Our phosphate detection system is a paper-based microfluidic device with an already-developed voltammetry device that automates the detection process so that any user can safely find phosphate in water. The system will provide a binary analysis about whether the water is safe to consume or not. Completion of the project provides a valuable tool to both average customers in developing countries and scientific researchers in determining the safety of drinking water

Ice Margins and Water Levels in Northwestern Vermon

Guidebook for field trips in Vermont: 64th annual meeting October 13, 14, 15, 1972 Burlington, Vermont: Trip G-2[1

Passive Immunotherapy in Alzheimer’s Disease

The development of therapeutics for the treatment of Alzheimer’s disease (AD) has been challenged with a myriad of obstacles: an evolving and incomplete understanding of disease etiology and progression, challenges with early diagnosis, multifactorial genetic and environmental factors that contribute to patient variability, and the cost of conducting lengthy clinical trials. One approach that has garnered a significant amount of attention and resources for its potential as a disease modifying approach is passive immunotherapy directed at clearing amyloid-β (Aβ) species, a pathological hallmark of Alzheimer’s disease. While passive immunotherapeutic trials directed at Aβ have not yet demonstrated clinical benefit, they have prompted important advances in the application and understanding of biomarkers, patient selection, novel functional readouts, and safety monitoring. Application of these lessons has enabled more recent clinical trials to incorporate better trial designs and refine inclusion criteria to optimize patient population enrollment. In addition, new passive immunotherapy targets emerging in the clinic have emerged, as well as novel technologies to enhance future antibody therapeutics. Taken together, the advances in research and clinical science have prepared the passive immunotherapy field to advance emerging promising disease modifying treatments in AD

Evidence Feed Forward Hidden Markov Model: A New Type of Hidden Markov Model

The ability to predict the intentions of people based solely on their visual actions is a skill only performed by humans and animals. The intelligence of current computer algorithms has not reached this level of complexity, but there are several research efforts that are working towards it. With the number of classification algorithms available, it is hard to determine which algorithm works best for a particular situation. In classification of visual human intent data, Hidden Markov Models (HMM), and their variants, are leading candidates. The inability of HMMs to provide a probability in the observation to observation linkages is a big downfall in this classification technique. If a person is visually identifying an action of another person, they monitor patterns in the observations. By estimating the next observation, people have the ability to summarize the actions, and thus determine, with pretty good accuracy, the intention of the person performing the action. These visual cues and linkages are important in creating intelligent algorithms for determining human actions based on visual observations. The Evidence Feed Forward Hidden Markov Model is a newly developed algorithm which provides observation to observation linkages. The following research addresses the theory behind Evidence Feed Forward HMMs, provides mathematical proofs of their learning of these parameters to optimize the likelihood of observations with a Evidence Feed Forwards HMM, which is important in all computational intelligence algorithm, and gives comparative examples with standard HMMs in classification of both visual action data and measurement data; thus providing a strong base for Evidence Feed Forward HMMs in classification of many types of problems.Comment: 19 pages, International Journal of Artificial Intelligence and Application

Feasibility and Design Studies: Camplain Valley Sanitary Landfill

Guidebook for field trips in Vermont: 64th annual meeting October 13, 14, 15, 1972 Burlington, Vermont: Trip EG-

Effects of differential jump training on balance performance in female volleyball players

The purpose of this study was to determine whether coordinative jump training that induces neuromuscular stimuli can affect balance performance, associated with injury risk, in elite-level female volleyball players. During the competitive season, the balance performance of 12 elite female players (highest Austrian division) was obtained via a wobble board (WB; 200 Hz) placed on an AMTI force plate (1000 Hz). Three identically repeated measurements defined two intervals (control and intervention phases), both comparable in duration and regular training. The intervention included 6 weeks of differential training (8 sessions of 15–20 min) that delivered variations in dynamics around the ankle joints. Multilevel mixed models were used to assess the effect on postural control. WB performance decreased from 27.0 ± 13.2% to 19.6 ± 11.3% during the control phase and increased to 54.5 ± 16.2% during the intervention (β = 49.1 ± 3.5; p < 0.001). Decreased sway area [cm²] (β = −7.5 ± 1.6; p < 0.001), anterior–posterior (β = −4.1 ± 0.4; p < 0.001) and mediolateral sway [mm] (β = −2.7 ± 0.6; p = 0.12), and mean velocity [mm∙s−1] (β = −9.0 ± 3.6; p < 0.05) were observed during the intervention compared with the control phase. Inter-limb asymmetry was reduced (β = −41.8 ± 14.4; p < 0.05). The applied training concept enhanced balance performance and postural control in elite female volleyball players. Due to the low additional physiological loads of the program and increased injury risk during the competitive season, we recommend this intervention for supporting injury prevention during this period

Influence of Log Length and Taper on Estimation of Hardwood Bof Position

The influence of log length and taper on hardwood BOF position by a centered-solution method was determined. A regression equation was developed to estimate opening face position as a function of the explanatory variables: centered-solution position, difference in T2 and T1 thickness, and log length and log taper. Coefficients of these explanatory variables were significant. However, examination of sums of squares showed that the centered-solution position adequately estimated BOF position. Tests of the power of the reduced equation proved it to be accurate in estimating BOF position and that average loss in board foot yield was less than one-half percent. These results show that a single equation based on the variable centered-solution position can accurately estimate BOF position for all length and taper classes of sawlogs

Cooperative Student Family Living: A History and Census of the Como Student Community.

Supported by the Center for Urban and Regional Affairs, University of Minnesota

Effects of Differential Jump Training on Balance Performance in Female Volleyball Players

The purpose of this study was to determine whether coordinative jump training that induces neuromuscular stimuli can affect balance performance, associated with injury risk, in elite-level female volleyball players. During the competitive season, the balance performance of 12 elite female players (highest Austrian division) was obtained via a wobble board (WB; 200 Hz) placed on an AMTI force plate (1000 Hz). Three identically repeated measurements defined two intervals (control and intervention phases), both comparable in duration and regular training. The intervention included 6 weeks of differential training (8 sessions of 15–20 min) that delivered variations in dynamics around the ankle joints. Multilevel mixed models were used to assess the effect on postural control. WB performance decreased from 27.0 ± 13.2% to 19.6 ± 11.3% during the control phase and increased to 54.5 ± 16.2% during the intervention (β = 49.1 ± 3.5; p < 0.001). Decreased sway area [cm²] (β = −7.5 ± 1.6; p < 0.001), anterior–posterior (β = −4.1 ± 0.4; p < 0.001) and mediolateral sway [mm] (β = −2.7 ± 0.6; p = 0.12), and mean velocity [mm∙s−1] (β = −9.0 ± 3.6; p < 0.05) were observed during the intervention compared with the control phase. Inter-limb asymmetry was reduced (β = −41.8 ± 14.4; p < 0.05). The applied training concept enhanced balance performance and postural control in elite female volleyball players. Due to the low additional physiological loads of the program and increased injury risk during the competitive season, we recommend this intervention for supporting injury prevention during this period