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Accurate estimates of pasture biomass in grazing lands are currently a time-consuming and resource-intensive task. The process generally includes physically cutting, bagging, labelling, drying, and weighing grass samples using multiple “quadrats” placed on the ground. Quadrats vary in size but are typically in the order of 0.25 m2 (i.e., 0.5 m × 0.5 m) up to 1.0 m2. Measurements from a number of harvested quadrats are then averaged to get a site estimate. This study investigated the use of photographs and ‘machine learning’ to reduce the time factor and difficulty in taking pasture biomass measurements to potentially make the estimations more accessible through the use of mobile phone cameras. A dataset was created from a pre-existing archive of quadrat photos and corresponding hand-cut pasture biomass measurements taken from a diverse range of field monitoring sites. Sites were clustered and one was held back per model for testing. The models were based on DenseNet121. Individual quadrat errors were large but more promising results were achieved when estimating the site mean pasture biomass. Another two smaller additional datasets were created post-training which were used to further assess the ensemble; they provided similar absolute errors to the original dataset, but significantly larger relative errors. The first was made from harvested quadrats, and the second was made using a pasture height meter in conjunction with a mobile phone camera. The models performed well across a variety of situations and locations but underperformed when assessed on some sites with very different vegetation. More data and refinement of the approach outlined in the paper will reduce the number of models needed and help to correct errors. These models provide a promising start, but further investigation, refinement, and data are needed before becoming a usable application.

This SoTL study aimed to determine thirty-eight teacher candidates’ self-efficacy after experiencing semester one of a yearlong residency. Researchers used a pre/post survey, the Teachers’ Sense of Efficacy Scale (TSES), and a retrospective pre TSES to determine perceived levels of self-efficacy in three areas: classroom management, instructional strategies, and student engagement. Findings revealed that pre TSES (candidates scored themselves at the beginning of the semester) and retrospective pre TSES scores (candidates scored themselves at the end of the semester reflecting on where they were at the beginning of the semester) were significantly different, with the retrospective pre TSES showing lower self-efficacy than the pre TSES. Post TSES comparisons to the retro-spective pre TSES showed a significant increase, whereas, the comparison of the traditional pre/post TSES showed no significant changes. This suggests teacher candidates had an overinflated sense of self-efficacy and that teacher educators should look beyond the pre TSES to provide transformational experiences for teacher candidates. The retro-pre TSES helps teacher educators monitor teacher candidates’ change in self-efficacy and gauge program impact.

Background:. Collagen nerve wraps (CNWs) theoretically allow for improved nerve gliding and decreased perineural scarring, and create a secluded environment to allow for nerve myelination and axonal healing. The goal of this study was to investigate the effect of CNWs on nerve gliding as assessed by pull-out strength and nerve changes in a rabbit model of peripheral neuropathy. Methods:. Ten New Zealand rabbits were included. Sham surgery (control) was performed on left hindlimbs. To simulate compressive neuropathy, right sciatic nerves were freed of the mesoneurium, and the epineurium was sutured to the wound bed. Five rabbits were euthanized at 6 weeks [scarred nerve (SN); n = 5]. Neurolysis with CNW was performed in the remaining rabbits at 6 weeks (CNW; n = 5), which were euthanized at 22 weeks. Outcomes included peak pull-out force and histopathological markers of nerve recovery (axonal and Schwann cell counts). Results:. The CNW group demonstrated significantly higher pull-out forces compared with the CNW sham control group (median: 4.40N versus 0.37N, P = 0.043) and a trend toward greater peak pull-out forces compared with the SN group (median: 4.40N versus 2.01N, P = 0.076). The CNW group had a significantly higher median Schwann cell density compared with the CNW control group (CNW: 1.30 × 10−3 cells/μm2 versus CNW control: 7.781 × 10−4 cells/μm2, P = 0.0431) and SN group (CNW: 1.30 × 10−3 cells/μm2 versus SN: 7.31 × 10−4 cells/μm2, P = 0.009). No significant difference in axonal density was observed between groups. Conclusion:. Our findings suggest using a CNW does not improve nerve gliding, but may instead play a role in recruiting and/or supporting Schwann cells and their proliferation.

Preclinical therapeutic assessment currently relies on the growth response of established human cell lines xenografted into immunocompromised mice, a strategy that is generally not predictive of clinical outcomes. Immunocompetent genetically engineered mouse (GEM)-derived tumor allograft models offer highly tractable preclinical alternatives and facilitate analysis of clinically promising immunomodulatory agents. Imageable reporters are essential for accurately tracking tumor growth and response, particularly for metastases. Unfortunately, reporters such as luciferase and GFP are foreign antigens in immunocompetent mice, potentially hindering tumor growth and confounding therapeutic responses. Here we assessed the value of reporter-tolerized GEMs as allograft recipients by targeting minimal expression of a luciferase-GFP fusion reporter to the anterior pituitary gland (dubbed the "Glowing Head" or GH mouse). The luciferase-GFP reporter expressed in tumor cells induced adverse immune responses in wildtype mouse, but not in GH mouse, as transplantation hosts. The antigenicity of optical reporters resulted in a decrease in both the growth and metastatic potential of the labeled tumor in wildtype mice as compared to the GH mice. Moreover, reporter expression can also alter the tumor response to chemotherapy or targeted therapy in a context-dependent manner. Thus the GH mice and experimental approaches vetted herein provide concept validation and a strategy for effective, reproducible preclinical evaluation of growth and response kinetics for traceable tumors.

Pasture biomass is an important quantity globally in livestock industries, carbon balances, and bushfire management. Quantitative estimates of pasture biomass or total standing dry matter (TSDM) at the field scale are much desired by land managers for land-resource management, forage budgeting, and conservation purposes. Estimates from optical satellite imagery alone tend to saturate in the cover-to-mass relationship and fail to differentiate standing dry matter from litter. X-band radar imagery was added to complement optical imagery with a structural component to improve TSDM estimates in rangelands. High quality paddock-scale field data from a northeastern Australian cattle grazing trial were used to establish a statistical TSDM model by integrating optical satellite image data from the Landsat sensor with observations from the TerraSAR-X (TSX) radar satellite. Data from the dry season of 2014 and the wet season of 2015 resulted in models with adjusted r2 of 0.81 in the dry season and 0.74 in the wet season. The respective models had a mean standard error of 332 kg/ha and 240 kg/ha. The wet and dry season conditions were different, largely due to changed overstorey vegetation conditions, but not greatly in a pasture ‘growth’ sense. A more robust combined-season model was established with an adjusted r2 of 0.76 and a mean standard error of 358 kg/ha. A clear improvement in the model performance could be demonstrated when integrating HH polarised TSX imagery with optical satellite image products.

Topoisomerase I (Top1) is a proven target for cancer therapeutics. Recent data from the Fluorouracil, Oxaliplatin, CPT-11: Use and Sequencing (FOCUS) trial demonstrated that nuclear staining of Top1 correlates with chemotherapeutic efficacy. Such a correlation may help identify patients likely to respond to Top1 inhibitors and illuminate their mechanism of action. Cellular response to Top1 inhibitors is complex, but Top1 target engagement is a necessary first step in this process. This paper reports the development and validation of a quantitative immunoassay for Top1 in tumors.We have developed and validated a two-site enzyme chemiluminescent immunoassay for quantifying Top1 levels in tumor biopsies. Analytical validation of the assay established the inter-day coefficient of variation at 9.3%±3.4% and a 96.5%±7.3% assay accuracy. Preclinical fit-for-purpose modeling of topotecan time- and dose-effects was performed using topotecan-responsive and -nonresponsive xenografts in athymic nude mice. Higher baseline levels of Top1 were observed in topotecan-responsive than -nonresponsive tumors. Top1 levels reached a maximal decrease 4 to 7 hours following treatment of engrafted mice with topotecan and the indenoisoquinoline NSC 724998.Our analysis of Top1 levels in control and treated tumors supports the previously proposed mechanism of action for Top1 inhibitor efficacy, wherein higher baseline Top1 levels lead to formation of more covalent-complex-dependent double-strand break damage and, ultimately, cell death. In contrast, xenografts with lower baseline Top1 levels accumulate fewer double-stand breaks, and may be more resistant to Top1 inhibitors. Our results support further investigation into the use of Top1 levels in tumors as a potential predictive biomarker. The Top1 immunoassay described in this paper has been incorporated into a Phase I clinical trial at the National Cancer Institute to assess pharmacodynamic response in tumor biopsies and determine whether baseline Top1 levels are predictive of response to indenoisoquinoline Top1 inhibitors.