Trailer Park Residents: Are They Worthy of Society's Respect?

Existing research is limited in explaining the existence of and reasons for stereotypes held against trailer park residents. This study uses an experimental design to measure attitudes towards trailer park residents, specifically in terms of being considered worthy of society’s respect. An Internet questionnaire was designed and administered to a sample of 559 introductory sociology students at a Midwestern university using semantic differentials to measure attitudes towards a fictitious couple. Participants were divided into a control and an experimental group. The groups were presented with two different vignettes, which were the same except for the experimental group, for which the vignette contained the term trailer park as a descriptor. The results indicated that differences between the control group and the experimental group on several measures were significant. The results supported the hypothesis that those who live in trailer parks were deemed less worthy of society’s respect by college students. In conclusion, the findings support the notion that people who live in trailer parks have been singled out in American culture for denigration

Opportunity and Means: Horizontal Gene Transfer from the Human Host to a Bacterial Pathogen

The acquisition and incorporation of genetic material between nonmating species, or horizontal gene transfer (HGT), has been frequently described for phylogenetically related organisms, but far less evidence exists for HGT between highly divergent organisms. Here we report the identification and characterization of a horizontally transferred fragment of the human long interspersed nuclear element L1 to the genome of the strictly human pathogen Neisseria gonorrhoeae. A 685-bp sequence exhibiting 98 to 100% identity to copies of the human L1 element was identified adjacent to the irg4 gene in some N. gonorrhoeae genomes. The L1 fragment was observed in ~11% of the N. gonorrhoeae population sampled but was not detected in Neisseria meningitidis or commensal Neisseria isolates. In addition, N. gonorrhoeae transcripts containing the L1 sequence were detected by reverse transcription-PCR, indicating that an L1-derived gene product may be produced. The high degree of identity between human and gonococcal L1 sequences, together with the absence of L1 sequences from related Neisseria species, indicates that this HGT event occurred relatively recently in evolutionary history. The identification of L1 sequences in N. gonorrhoeae demonstrates that HGT can occur between a mammalian host and a resident bacterium, which has important implications for the coevolution of both humans and their associated microorganisms

Introducing Fuzzy Layers for Deep Learning

Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.Comment: 6 pages, 4 figures, published in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE

Evaluation of Manual Ultrasonic Examinations Applied to Detect Flaws in Primary System Dissimilar Metal Welds at North Anna Power Station

During a recent inservice inspection (ISI) of a dissimilar metal weld (DMW) in an inlet (hot leg) steam generator nozzle at North Anna Power Station Unit 1, several axially oriented flaws went undetected by the licensee's manual ultrasonic testing (UT) technique. The flaws were subsequently detected as a result of outside diameter (OD) surface machining in preparation for a full structural weld overlay. The machining operation uncovered the existence of two through-wall flaws, based on the observance of primary water leaking from the DMW. Further ultrasonic tests were then performed, and a total of five axially oriented flaws, classified as primary water stress corrosion cracking (PWSCC), were detected in varied locations around the weld circumference

Diabetes management before and after cancer diagnosis: missed opportunity

Background Few studies have examined the management of comorbidities in cancer patients. This study used population-based data to estimate the guideline concordance rates for diabetes management before and after cancer diagnosis and examined if diabetes management services among cancer patients was associated with characteristics of the hospital where the patient was treated. Methods We linked 2005-2009 Medicare claims data to information on 2,707 breast and colorectal cancers patients in state cancer registry files. Multivariate logistic regression models examined hospital characteristics associated with receipt of diabetes management care after cancer diagnosis. Results The rates of HbAlc testing, LDL-C testing, and retinal eye exam decreased from 72.7%, 79.6%, and 57.9% before cancer diagnosis to 58.3%, 69.5%, and 55.8% after diagnosis. The pre- and post-diagnosis diabetes management care was not significantly different by hospital characteristics in the bivariate analysis except for that the distance between residence and hospital was negatively related to retinal eye exam after diagnosis (P Conclusions Cancer patients received fewer diabetes management care after diagnosis than prior to diagnosis, even for those who were treated in large comprehensive centers. This may reflect a missed opportunity to connect diabetic cancer patients to diabetes care. This study provides benchmarks to measure improvements in comorbidity management among cancer patients

Kernel Matrix-Based Heuristic Multiple Kernel Learning

Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods

Complementary Lenses: Using Theories of Situativity and Complexity to Understand Collaborative Learning as Systems-Level Social Activity

This article highlights possibilities for understanding challenges related to collaborative learning by bringing two complementary lenses into theoretical and empirical conversation—complexity and situativity. After presenting a theoretical comparison that characterizes complementarity between complexity and situativity in order to frame their relative contributions to a systems-level understanding of learning processes, we examine persistently unproductive social activity during a 14-session, collaborative engineering design project in a fifth-grade peer group from both perspectives. We do so in order to demonstrate the value of these complementary perspectives for understanding collaborative learning processes and to suggest different explanations of why unproductive social activity sometimes persists and possibilities for interrupting such dynamics. We thus suggest a shift from explanatory accounts of system processes to prospective processes for systems of action within social ecologies of change. Such a framework can resolve the social activity of collaborative learning around a systems-level orientation

Neural crest stem cells undergo multilineage differentiation in developing peripheral nerves to generate endoneurial fibroblasts in addition to Schwann cells

Neural crest stem cells (NCSCs) persist in peripheral nerves throughout late gestation but their function is unknown. Current models of nerve development only consider the generation of Schwann cells from neural crest, but the presence of NCSCs raises the possibility of multilineage differentiation. We performed Cre-recombinase fate mapping to determine which nerve cells are neural crest derived. Endoneurial fibroblasts, in addition to myelinating and non-myelinating Schwann cells, were neural crest derived, whereas perineurial cells, pericytes and endothelial cells were not. This identified endoneurial fibroblasts as a novel neural crest derivative, and demonstrated that trunk neural crest does give rise to fibroblasts in vivo, consistent with previous studies of trunk NCSCs in culture. The multilineage differentiation of NCSCs into glial and non-glial derivatives in the developing nerve appears to be regulated by neuregulin, notch ligands, and bone morphogenic proteins, as these factors are expressed in the developing nerve, and cause nerve NCSCs to generate Schwann cells and fibroblasts, but not neurons, in culture. Nerve development is thus more complex than was previously thought, involving NCSC self-renewal, lineage commitment and multilineage differentiation