Public Perceptions of Wisconsin’s Pavements and Tradeoffs in Pavement Improvement

Findings are reported from Phase II of a three-phase pooled-fund project in Wisconsin, Iowa, and Minnesota to determine perceptions of drivers regarding pavement of rural two-lane highways. Among the survey topics were drivers\u27 trust in the state department of transportation (DOT), pavement improvement trade-offs, and pavement evaluation. Results of the Wisconsin portion of the survey data are the focus of this study. The survey questionnaire was based in part on Phase I focus groups conducted to gauge beliefs about pavements as well as the language describing ruts, tining, and other pavement characteristics. Phase II entailed a statewide telephone survey of at least 400 randomly selected drivers in each of the three states. Although the focus here is on Wisconsin results, survey responses across the three states were very consistent. Included in the findings discussed are perceptions of pavement and the state DOT and pavement improvement options relating to construction, travel time, and delays. Results disclose key public perceptions of priorities with regard to spending limited funds. Also discussed are statistically significant relationships providing additional insights into public perceptions and pavement improvement on rural two-lane highways

Students’ Acquisition of Agricultural and Entrepreneurship (Agripreneurship) Knowledge and Skills: Does Instructional Approach and their Sex Matter?

Agricultural and Entrepreneurship education are interdisciplinary due to amalgamating the natural sciences and social sciences. These disciplines have gained the interest of those looking to equip youth with skills for self-reliance. Teachers employ various instructional approaches, including student-centric approaches such as project-based learning (PjBL) and teacher-centric methods, for example, the lecture method, to facilitate learning. Existing research, however, suggests that students’ learning can be influenced by other factors, for example, learning styles, socio-cultural norms, sex stereotypes, and the instructional approach(es) used. We examined the impact of using the lecture method (counterfactual group) versus PjBL (treatment group) approaches on student acquisition of agricultural knowledge in the context of poultry science and their intentions to become agripreneurs. A statistically significant disordinal interaction with a medium effect size was found at p \u3c .05 between groups and student sex for poultry science knowledge. The female students performed better under the PjBL, while the males did so under lecture-based instruction. We also found a statistically significant and positive (p \u3c .05) association between students’ sex and their intent to become agripreneurs for the treatment group. More female students than males in the treatment group indicated they were either likely or highly likely to become agripreneurs in the future. These findings imply that females in the treatment group benefited more from the intervention, PjBL, than their male peers. Additional research should be conducted to measure the long-term impact of using various teaching approaches on students’ learning of agriculture and entrepreneurship content depending on their sex

Tracking TCRß sequence clonotype expansions during antiviral therapy using high-throughput sequencing of the hypervariable region

To maintain a persistent infection viruses such as hepatitis C virus (HCV) employ a range of mechanisms that subvert protective T cell responses. The suppression of antigen-specific T cell responses by HCV hinders efforts to profile T cell responses during chronic infection and antiviral therapy. Conventional methods of detecting antigen-specific T cells utilize either antigen stimulation (e.g., ELISpot, proliferation assays, cytokine production) or antigen-loaded tetramer staining. This limits the ability to profile T cell responses during chronic infection due to suppressed effector function and the requirement for prior knowledge of antigenic viral peptide sequences. Recently, high-throughput sequencing (HTS) technologies have been developed for the analysis of T cell repertoires. In the present study, we have assessed the feasibility of HTS of the TCRβ complementarity determining region (CDR)3 to track T cell expansions in an antigen-independent manner. Using sequential blood samples from HCV-infected individuals undergoing antiviral therapy, we were able to measure the population frequencies of >35,000 TCRβ sequence clonotypes in each individual over the course of 12 weeks. TRBV/TRBJ gene segment usage varied markedly between individuals but remained relatively constant within individuals across the course of therapy. Despite this stable TRBV/TRBJ gene segment usage, a number of TCRβ sequence clonotypes showed dramatic changes in read frequency. These changes could not be linked to therapy outcomes in the present study; however, the TCRβ CDR3 sequences with the largest fold changes did include sequences with identical TRBV/TRBJ gene segment usage and high junction region homology to previously published CDR3 sequences from HCV-specific T cells targeting the HLA-B*0801-restricted 1395HSKKKCDEL1403 and HLA-A*0101-restricted 1435ATDALMTGY1443 epitopes. The pipeline developed in this proof of concept study provides a platform for the design of future experiments to accurately address the question of whether T cell responses contribute to SVR upon antiviral therapy. This pipeline represents a novel technique to analyze T cell dynamics in situations where conventional antigen-dependent methods are limited due to suppression of T cell functions and highly diverse antigenic sequences

The canopy effects of Prosopis juliflora (DC) and Acacia tortilis (Hayne) trees on herbaceous plants species and soil physico-chemical properties in Njemps Flats, Kenya

The canopy effects of an exotic and indigenous tree species on soil properties and understorey herbaceous plant species were investigated on the Njemps Flats, Baringo district, Kenya. Samples of soil and herbaceous plant species were obtained within the canopies of systematically selected P. juliflora (exotic) and A. tortilis (indigenous) trees, and from adjacent open areas. Standing biomass, frequency and cover of understorey plant species were significantly (P<0.05) higher in the open area than under the canopies. Cover for herbaceous plant species was 63% under P. juliflora, 82% under A. tortilis and 90% in open areas. All forbs occurred under the canopies indicating that they are more adapted to the shaded micro environments than grasses. Soils under the tree canopies had significantly (P<0.05) higher organic carbon and total nitrogen than those in adjacent open areas. Soils under A. tortilis had significantly (P<0.05) higher organic carbon and total nitrogen than soils from under P. juliflora. The results suggested that A. tortilis trees are more beneficial to soil physical and chemical properties than P. juliflora. Accordingly, the common practice of clearing woody trees indiscriminately to improve grassland for livestock production or for crop cultivation should not be recommended

Identifying what makes a good question in a mechanics diagnostic test

Many students entering engineering degrees encounter problems with the mathematics involved. More recently, research has shown that freshers may have insuffi cient knowledge of mechanics. In order to assess this, the authors created and administered a multiple-choice mechanics diagnostic test. This paper gives details of the test, and evaluates, using item analysis, how students performed on the questions and on the topics assessed by it. It also makes recommendations for devising questions which allow a diagnostic test to discriminate between students

Predicting performance of 1st year engineering students and the importance of assessment tools therein

In recent years, the increase in the number of people entering university has contributed to a greater variability in the background of those beginning programmes. Consequently, it has become even more important to understand a student’s prior knowledge of a given subject. Two main reasons for this are to produce a suitable first year curriculum and to ascertain whether a student would benefit from additional support. Therefore, in order that any necessary steps can be taken to improve a student’s performance, the ultimate goal would be the ability to predict future performance. A continuing change in students’ prior mathematics (and mechanics) knowledge is being seen in engineering, a subject that requires a significant amount of mathematics knowledge. This paper describes statistical regression models used for predicting students’ first year performance. Results from these models highlight that a mathematics diagnostic test is not only useful for gaining information on a student’s prior knowledge but is also one of the best predictors of future performance. In the models, it was also found that students’ marks could be improved by seeking help in the university’s mathematics learning support centre. Tools and methodologies (e.g. surveys and diagnostic tests) suitable for obtaining data used in the regression models are also discussed

ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping

Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present a novel framework for creating class specific FA maps through image-to-image translation. We propose the use of a VAE-GAN to explicitly disentangle class relevance from background features for improved interpretability properties, which results in meaningful FA maps. We validate our method on 2D and 3D brain image datasets of dementia (ADNI dataset), ageing (UK Biobank), and (simulated) lesion detection. We show that FA maps generated by our method outperform baseline FA methods when validated against ground truth. More significantly, our approach is the first to use latent space sampling to support exploration of phenotype variation. Our code will be available online at https://github.com/CherBass/ICAM.Comment: Submitted to NeurIPS 2020: Neural Information Processing Systems. Keywords: interpretable, classification, feature attribution, domain translation, variational autoencoder, generative adversarial network, neuroimagin

Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics

We outline a general strategy for the application of edit- distance based kernels to NP Theorem Proving in order to allow for polynomial-time machine learning of proof heuristics without the loss of sequential structural information associated with conventional feature- based machine learning. We provide a general short introduction to logic and proof considering a few important complexity results to set the scene and highlight the relevance of our findings

Bringing Agriculture into the GATT: Assessing the Benefits of Trade Liberalization

International Relations/Trade,