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By the mid-twentieth century, wolves were nearly extinct in the lower 48 states, with a small number surviving in northern Minnesota. After wolves were placed on the endangered species list in 1973, the northern Minnesota wolf population increased and stabilized by the early 2000s. A wolf trophy hunt was introduced in 2012–2014 and then halted by a court order in December 2014. The Minnesota Department of Natural Resources collected wolf radiotelemetry data for the years 2004–2019. Statistical analysis showed that wolf mortality remained close to constant from 2004 until the initiation of the hunt, and that mortality doubled with the initiation of the first hunting and trapping season in 2012, remaining at a nearly constant elevated level through 2019. Notably, average annual wolf mortality increased from 21.7% before wolf hunting seasons (10.0% by human causes and 11.7% natural causes) to 43.4% (35.8% by human causes and 7.6% natural causes). The fine-grained statistical trend implies that human-caused mortality increased sharply during the hunting seasons, while natural mortality initially dropped. After the hunt's discontinuation, human-caused mortality remained higher than prior to the hunting seasons throughout the five years of the available after-hunt radiotelemetry data. [ABSTRACT FROM AUTHOR]

Diagnostic tools for the detection of protein-misfolding diseases (i.e., proteopathies) are limited. Gold nanoparticles (AuNPs) facilitate sensitive diagnostic techniques via visual color change for the identification of a variety of targets. In parallel, recently developed quaking-induced conversion (QuIC) assays leverage protein-amplification and fluorescent signaling for the accurate detection of misfolded proteins. Here, we combine AuNP and QuIC technologies for the visual detection of amplified misfolded prion proteins from tissues of wild white-tailed deer infected with chronic wasting disease (CWD), a prion disease of cervids. Our newly developed assay, MN-QuIC, enables both naked-eye and light-absorbance measurements for detection of misfolded prions. MN-QuIC leverages basic laboratory equipment that is cost-effective and portable, thus facilitating real-time prion diagnostics across a variety of settings. In addition to laboratory-based tests, we deployed to a rural field-station in southeastern Minnesota and tested for CWD on site. We successfully demonstrated that MN-QuIC is functional in a non-traditional laboratory setting by performing a blinded analysis in the field and correctly identifying all CWD positive and CWD not-detected deer at the field site in 24 h, thus documenting the portability of the assay. White-tailed deer tissues used to validate MN-QuIC included medial retropharyngeal lymph nodes, parotid lymph nodes, and palatine tonsils. Importantly, all of the white-tailed deer (n = 63) were independently tested using ELISA, IHC, and/or RT-QuIC technologies and results secured with MN-QuIC were 95.7% and 100% consistent with these tests for positive and non-detected animals, respectively. We hypothesize that electrostatic forces help govern the AuNP/prion interactions and conclude that MN-QuIC has great potential for sensitive, field-deployable diagnostics for CWD, with future potential diagnostic applications for a variety of proteopathies. [ABSTRACT FROM AUTHOR]

The environment plays a key role in the spread and persistence of antimicrobial resistance (AMR). Antimicrobials and antimicrobial resistance genes (ARG) are released into the environment from sources such as wastewater treatment plants, and animal farms. This study describes an approach guided by spatial mapping to quantify and predict antimicrobials and ARG in Minnesota's waterbodies in water and sediment at two spatial scales: macro, throughout the state, and micro, in specific waterbodies. At the macroscale, the highest concentrations across all antimicrobial classes were found near populated areas. Kernel interpolation provided an approximation of antimicrobial concentrations and ARG abundance at unsampled locations. However, there was high uncertainty in these predictions, due in part to low study power and large distances between sites. At the microscale, wastewater treatment plants had an effect on ARG abundance (sul1 and sul2 in water; blaSHV, intl1, mexB, and sul2 in sediment), but not on antimicrobial concentrations. Results from sediment reflected a long-term history, while water reflected a more transient record of antimicrobials and ARG. This study highlights the value of using spatial analyses, different spatial scales, and sampling matrices, to design an environmental monitoring approach to advance our understanding of AMR persistence and dissemination. [ABSTRACT FROM AUTHOR]

In children with Down syndrome (DS) development of visual, motor and cognitive functions is atypical. It is unknown whether the visual impairments in children with DS aggravate their lag in cognitive development. Visual impairment and developmental lags in adaptive behaviour and executive functions were assessed in 104 children with DS, 2–16 years, by comparing their adaptive behaviour, executive functions and visual acuity (distant and near) scores against published age-matched norm scores of typically developing children. Associations between these lags were explored. Mean (± SEM) differences to age-matched norms indicated reduced performance in DS: Vineland Screener questionnaire, − 63 ± 3.8 months; task-based Minnesota Executive Function Scale (MEFS), − 46.09 ± 2.07 points; BRIEF-P questionnaire, 25.29 ± 4.66 points; BRIEF parents' and teachers' questionnaire, 17.89 ± 3.92 points and 40.10 ± 3.81 points; distant and near visual acuity, 0.51 ± 0.03 LogMAR and 0.63 ± 0.03 LogMAR (near − 0.11 ± 0.04 LogMAR poorer than distant). Adaptive behaviour (Vineland-S) correlated with the severity of visual impairment (r = − 0.396). Children with DS are severely impaired in adaptive behaviour, executive functions and visual acuities (near visual acuity more severely impaired than distant visual acuity). Larger impairment in adaptive behaviour is found in children with larger visual impairment. This supports the idea that visual acuity plays a role in adaptive development. [ABSTRACT FROM AUTHOR]

Invasive plants are an ongoing subject of interest in North American forests, owing to their impacts on forest structure and regeneration, biodiversity, and ecosystem services. An important component of studying and managing forest invaders involves knowing where the species are, or could be, geographically located. Temporal and environmental context, in conjunction with spatially-explicit species occurrence information, can be used to address this need. Here, we predict the potential current and future distributions of four forest plant invaders in Minnesota: common buckthorn (Rhamnus cathartica), glossy buckthorn (Frangula alnus), garlic mustard (Alliaria petiolata), and multiflora rose (Rosa multiflora). We assessed the impact of two different climate change scenarios (IPCC RCP 6.0 and 8.5) at two future timepoints (2050s and 2070s) as well as the importance of occurrence data sources on the potential distribution of each species. Our results suggest that climate change scenarios considered here could result in a potential loss of suitable habitat in Minnesota for both buckthorn species and a potential gain for R. multiflora and A. petiolata. Differences in predictions as a result of input occurrence data source were most pronounced in future climate projections. [ABSTRACT FROM AUTHOR]

We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as 'single SNP association analysis'. Joint modeling of genetic variants within a gene or pathway may have better power to detect associated genetic variants, especially the ones with weak effects. In this paper, we propose a computationally efficient model selection approach-based on the e-values framework-for single SNP detection in families while utilizing information on multiple SNPs simultaneously. To overcome computational bottleneck of traditional model selection methods, our method trains one single model, and utilizes a fast and scalable bootstrap procedure. We illustrate through numerical studies that our proposed method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. Further, we perform gene-level analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using our method to detect several SNPs using this that have been implicated to be associated with alcohol consumption., (© 2023. The Author(s).)