Stevens County Food Assessment

This report is the culmination of a year-long community food assessment conducted by staff, students, and faculty at the University of Minnesota Morris, and informed by an advisory council made up of key local stakeholders. The main goal of the community food assessment is to describe food security in Stevens County at both community and individual scales. This assessment examines what food is grown in the county, what food is available, where food can be obtained in various forms, accessibility and affordability of food, as well as county residents’ experiences with and thoughts and suggestions about food. Findings summarized below rely on several different types of data, including a household food security survey, a survey of prices and availability at area grocery stores, personal communications and observations, and secondary data (e.g., from the US Census Bureau). More details about data collection and the key findings presented below are available in the full version of this report. Based on the (available and newly collected) data for this community food assessment, it is clear that Stevens County does not fit the definition of community food security because many residents are food insecure, food insecure residents tend to share characteristics of marginalized populations, and little of the food consumed in Stevens County is produced and processed in Stevens County. Challenges with community food security are of course not necessarily uniquely to Stevens County, MN as they are at least in part a product of the way our regional, national, and global food supply chains presently function.https://digitalcommons.morris.umn.edu/cst/1083/thumbnail.jp

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Effect of pre-plant treatments of yam (Dioscorea rotundata) setts on the production of healthy seed yam, seed yam storage and consecutive ware tuber production

Numerous pests and diseases of yams are perpetuated from season to season through the use of infected seed material. Developing a system for generating healthy seed material would disrupt this disease cycle and reduce losses in field and storage. The use of various pre-plant treatments was evaluated in field experiments carried out at three sites in Nigeria. Yam tubers of four preferred local cultivars were cut into 100 g setts and treated with pesticide (fungicide + insecticide mixture), neem extract (1 : 5 w/v), hot water (20 min at 53 °C) or wood ash (farmers practice) and compared with untreated setts. Pesticide treated setts sprouted better than all other treatments and generally led to lower pest and disease damage of yam tubers. Pesticide treatment increased tuber yields over most treatments, depending on cultivar, but effectively doubled the production as compared to the control. Pesticide and hot water treated setts produced the healthiest seed yams, which had lower storage losses than tubers from other treatments. These pre-treated seed yams produced higher yields corresponding to 700 % potential gain compared to the farmers usual practice. Treatments had no obvious influence on virus incidence, although virus-symptomatic plants yielded significantly less than nonsymptomatic plants. This study demonstrated that pre-plant treatment of setts with pesticide is a simple and effective method that guarantees more, heavier and healthier seed yam tubers

Recent loss of Gibraltar seagrasses

Worldwide, seagrasses face threats including climate change, disease and anthropogenic disturbance, with populations at the extremes of species' distributions likely presaging future problems elsewhere in their geographical ranges. At the geographic limits of two marine macrophytes (Zostera marina and Posidonia oceanica) and under intense urbanization, seagrasses around Gibraltar are particularly vulnerable. However, the last published survey of Gibraltar seagrass meadows, in 1993, showed both species were abundant. We resurveyed this area and were unable to locate any seagrass in Gibraltar waters. Extensive coastal development and land reclamation make much former seagrass habitat in Gibraltar waters unsuitable, presenting substantial hurdles to any future restoration efforts

Automated chemical identification and library building using dispersion plots for differential mobility spectrometry

Differential mobility spectrometry (DMS) based detectors require rapid data analysis capabilities, embedded into the devices to achieve the optimum detection capabiites as portable trace chemical detectors. Automated algorithm-based DMS dispersion plot data analysis method was applied for the first time to pre-process and separate 3-dimentional (3-D) DMS dispersion data. We previously demonstrated our AnalyzeIMS (AIMS) software was capable of analyzing complex gas chromatography differential mobility spectrometry (GC-DMS) data sets. In our present work, the AIMS software was able to easliy separate DMS dispersion data sets of five chemicals that are important in detection of volatile organic compounds (VOCs): 2-butanone, 2-propanone, ethyl acetate, methanol and ethanol. Identification of chemicals from mixtures, separation of chemicals from a mixture and prediction capability of the software were all tested. These automated algorithms may have potential applications in separation of chemicals (or ion peaks) from other 3-D data obtained by hybrid analytical devices such as mass spectrometry (MS). New algorithm developments are included as future considerations to improve the current numerical approaches to fingerprint chemicals (ions) from a significantly complicated dispersion plot. Comprehensive peak identifcation by DMS-MS, variations of the DMS data due to chemical concentration, gas phase ion chemistry, temperature and pressure of the drift gas are considered in future algorithm improvements

Peak detection and random forests classification software for gas chromatography/differential mobility spectrometry (GC/DMS) data.

Gas Chromatography/Differential Mobility Spectrometry (GC/DMS) is an effective tool to discern volatile chemicals. The process of correlating GC/DMS data outputs to chemical identities requires time and effort from trained chemists due to lack of commercially available software and the lack of appropriate libraries. This paper describes the coupling of computer vision techniques to develop models for peak detection and can align chemical signatures across datasets. The result is an automatically generated peak table that provides integrated peak areas for the inputted samples. The software was tested against a simulated dataset, whereby the number of detected features highly correlated to the number of actual features (r2 = 0.95). This software has also been developed to include random forests, a discriminant analysis technique that generates prediction models for application to unknown samples with different chemical signatures. In an example dataset described herein, the model achieves 3% classification error with 12 trees and 0% classification error with 48 trees. The number of trees can be optimized based on the computational resources available. We expect the public release of this software can provide other GC/DMS researchers with a tool for automated featured extraction and discriminant analysis capabilities

Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures.

Gas-phase trace chemical detection techniques such as ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) can be used in many settings, such as evaluating the health condition of patients or detecting explosives at airports. These devices separate chemical compounds in a mixture and provide information to identify specific chemical species of interest. Further, these types of devices operate well in both controlled lab environments and in-field applications. Frequently, the commercial versions of these devices are highly tailored for niche applications (e.g., explosives detection) because of the difficulty involved in reconfiguring instrumentation hardware and data analysis software algorithms. In order for researchers to quickly adapt these tools for new purposes and broader panels of chemical targets, it is critical to develop new algorithms and methods for generating libraries of these sensor responses. Microelectromechanical system (MEMS) technology has been used to fabricate DMS devices that miniaturize the platforms for easier deployment; however, concurrent advances in advanced data analytics are lagging. DMS generates complex three-dimensional dispersion plots for both positive and negative ions in a mixture. Although simple spectra of single chemicals are straightforward to interpret (both visually and via algorithms), it is exceedingly challenging to interpret dispersion plots from complex mixtures with many chemical constituents. This study uses image processing and computer vision steps to automatically identify features from DMS dispersion plots. We used the bag-of-words approach adapted from natural language processing and information retrieval to cluster and organize these features. Finally, a support vector machine (SVM) learning algorithm was trained using these features in order to detect and classify specific compounds in these represented conceptualized data outputs. Using this approach, we successfully maintain a high level of correct chemical identification, even when a gas mixture increases in complexity with interfering chemicals present

Wearable Sensor System to Monitor Physical Activity and the Physiological Effects of Heat Exposure.

Mobile health monitoring via non-invasive wearable sensors is poised to advance telehealth for older adults and other vulnerable populations. Extreme heat and other environmental conditions raise serious health challenges that warrant monitoring of real-time physiological data as people go about their normal activities. Mobile systems could be beneficial for many communities, including elite athletes, military special forces, and at-home geriatric monitoring. While some commercial monitors exist, they are bulky, require reconfiguration, and do not fit seamlessly as a simple wearable device. We designed, prototyped and tested an integrated sensor platform that records heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response. The device uses a small microcontroller to integrate the measurements and store data directly on the device for up to 48+ h. continuously. The device was compared to clinical standards for calibration and performance benchmarking. We found that our system compared favorably with clinical measures, such as fingertip pulse oximetry and infrared thermometry, with high accuracy and correlation. Our novel platform would facilitate an individualized approach to care, particularly those whose access to healthcare facilities is limited. The platform also can be used as a research tool to study physiological responses to a variety of environmental conditions, such as extreme heat, and can be customized to incorporate new sensors to explore other lines of inquiry

An environmental air sampler to evaluate personal exposure to volatile organic compounds

A micro fabricated chip-based wearable air sampler was used to monitor the personnel exposure of volatile chemical concentrations in microenvironments. Six teenagers participated in this study and 14 volatile organic compounds (VOCs) including naphthalene, 3-decen-1-ol, hexanal, nonanal, methyl salicylate and limonene gave the highest abundance during routine daily activity. VOC exposure associated with daily activities and the location showed strong agreements with two of the participant's results. One of these subjects had the highest exposure to methyl salicylate that was supported by the use of a topical analgesic balm containing this compound. Environmental based air quality monitoring followed by the personnel exposure studies provided additional evidence associated to the main locations where the participants traveled. Toluene concentrations observed at a gas station were exceptionally high, with the highest amount observed at 1213.1 ng m-3. One subject had the highest exposure to toluene and the GPS data showed clear evidence of activities neighboring a gas station. This study shows that this wearable air sampler has potential applications including hazardous VOC exposure monitoring in occupational hazard assessment for certain professions, for example in industries that involve direct handling of petroleum products