Use of Online Tools in Teaching C++ Programming to Freshmen in All Engineering Majors

As computer software becomes increasingly used in analysis and design in all engineering disciplines, more engineering programs have started including computer programming in their common core for all engineering majors. C++ is a popular programming language that’s been chosen for teaching engineering students programming. At California Baptist University, EGR 121 Introduction to Computer Programming in C++ is a required course for all engineering students. Most of our engineering students take this course in their first year. This course was taught using traditional means of lecture, text book reading and exercises along with labs and programming projects. Since the fall of 2012 we incorporated two online resources, an online interactive content resource and an online exercise tool to replace the previous textbook problems as homework. We discuss our experience in the classroom along with survey feedback from our students. Although no statistically significant difference in final grades was detected, we did find anecdotal indication that students benefited from these tools particularly the online homework problems

On switching response surface models, with applications to the structural health monitoring of bridges

Structural Health Monitoring (SHM) is the engineering discipline of diagnosing damage and estimating safe remaining life for structures and systems. Often, SHM is accomplished by detecting changes in measured quantities from the structure of interest; if there are no competing explanations for the changes, one infers that they are the result of damage. If the structure of interest is subject to changes in its environmental or operational conditions, one must understand the effects of these changes in order that one does not falsely claim that damage has occurred when changes in measured quantities are observed. This problem – the problem of confounding influences – is particularly pressing for civil infrastructure where the given structure is usually openly exposed to the weather and may be subject to strongly varying operational conditions. One approach to understanding confounding influences is to construct a data-based response surface model that can represent measurement variations as a function of environmental and operational variables. The models can then be used to remove environmental and operational variations so that change detection algorithms signal the occurrence of damage alone. The current paper is concerned with such response surface models in the case of SHM of bridges. In particular, classes of response surface models that can switch discontinuously between regimes are discussed. Recently, it has been shown that Gaussian Process (GP) models are an effective means of developing response surface or surrogate models. However, the GP approach runs into difficulties if changes in the latent variables cause the structure of interest to abruptly switch between regimes. A good example here, which is well known in the SHM literature, is given by the Z24 Bridge in Switzerland which completely changed its dynamical behaviour when it cooled below zero degrees Celsius as the asphalt of the deck stiffened. The solution proposed here is to adopt the recently-proposed Treed Gaussian Process (TGP) model as an alternative. The approach is illustrated here on the Z24 bridge and also on data from the Tamar Bridge in the UK which shows marked switching behaviour in certain of its dynamical characteristics when its ambient wind conditions change. It is shown that treed GPs provide an effective approach to response surface modelling and that in the Tamar case, a linear model is in fact sufficient to solve the problem

On-the-go hyperspectral imaging under field conditions and machine learning for the classification of grapevine varieties

Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry. This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine (Vitis vinifera L.) varieties under field conditions using on-the-go hyperspectral imaging and different machine learning algorithms. On-the-go imaging was performed under natural illumination using a hyperspectral camera mounted on an all-terrain vehicle at 5 km/h. Spectra were acquired over two different leaf phenological stages on the canopy of 30 different varieties on a commercial vineyard located in La Rioja, Spain. A total of 1,200 spectral samples were generated. Support vector machines (SVM) and artificial neural networks (multilayer perceptrons, MLP) were used for the development of a large number of models, testing different algorithm parameters and spectral pre-processing techniques. Both classifiers yielded notable performance values and were able to train models with recall F1 scores and area under the receiver operating characteristic curve marks up to 0.99 for 5-fold cross validation. Statistical analyses supported that the best SVM kernel was linear and the best activation function for MLP was the hyperbolic tangent function. The prediction performance for individual varieties of MLP ranged from 0.94 to 0.99, displaying low levels of variability. In the case of SVM, slightly higher differences were obtained, ranging from 0.83 to 0.97 for individual varieties. These results support the possibility of deploying an on-the-go hyperspectral imaging system in the field capable of successfully classifying leaves from different grapevine varieties. This technology could thus be considered as a new useful non-destructive tool for plant phenotyping under field conditions.The work leading to these results has received funding from the European Union under grant agreement number 737669 (VINESCOUT project). SG would like to acknowledge the research funding FPI grant 299/2016 by Universidad de La Rioja, Gobierno de La Rioja. MD is funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) with a Ramon y Cajal grant RYC-2015-18429.Peer reviewe

A BAFF-R mutation associated with non-Hodgkin lymphoma alters TRAF recruitment and reveals new insights into BAFF-R signaling

A BAFF receptor mutation associated with non-Hodgkin lymphoma provides new insight into the proximal players of normal BAFF-R signaling

CESTA, a positive regulator of brassinosteroid biosynthesis

Brassinosteroids are important plant hormones involved in the regulation of cell elongation, division, differentiation and development. This study identifies CESTA as a basic helix-loop-helix transcription factor that positively regulates brassinosteroid homeostasis

Identification of genes required for soil survival in Burkholderia thailandensis by transposon-directed insertion site sequencing.

Transposon-directed insertion site sequencing was used to identify genes required by Burkholderia thailandensis to survive in plant/soil microcosms. A total of 1,153 genetic loci fulfilled the criteria as being likely to encode survival characteristics. Of these, 203 (17.6 %) were associated with uptake and transport systems; 463 loci (40.1 %) coded for enzymatic properties, 99 of these (21.4 %) had reduction/oxidation functions; 117 (10.1 %) were gene regulation or sensory loci; 61 (5.3 %) encoded structural proteins found in the cell envelope or with enzymatic activities related to it, distinct from these, 46 (4.0 %) were involved in chemotaxis and flagellum, or pilus synthesis; 39 (3.4 %) were transposase enzymes or were bacteriophage-derived; and 30 (2.6 %) were involved in the production of antibiotics or siderophores. Two hundred and twenty genes (19.1 %) encoded hypothetical proteins or those of unknown function. Given the importance of motility and pilus formation in microcosm persistence the nature of the colonization of the rhizosphere was examined by confocal microscopy. Wild type B. thailandensis expressing red fluorescent protein was inoculated into microcosms. Even though the roots had been washed, the bacteria were still present but they were motile with no attachment having taken place, perhaps being retained in a biofilm

Development of an Acute and Highly Pathogenic Nonhuman Primate Model of Nipah Virus Infection

Nipah virus (NiV) is an enigmatic emerging pathogen that causes severe and often fatal neurologic and/or respiratory disease in both animals and humans. Amongst people, case fatality rates range between 40 and 75 percent and there are no vaccines or treatments approved for human use. Guinea pigs, hamsters, cats, ferrets, pigs and most recently squirrel monkeys (New World monkey) have been evaluated as animal models of human NiV infection, and with the exception of the ferret, no model recapitulates all aspects of NiV-mediated disease seen in humans. To identify a more viable nonhuman primate (NHP) model, we examined the pathogenesis of NiV in African green monkeys (AGM). Exposure of eight monkeys to NiV produced a severe systemic infection in all eight animals with seven of the animals succumbing to infection. Viral RNA was detected in the plasma of challenged animals and occurred in two of three subjects as a peak between days 7 and 21, providing the first clear demonstration of plasma-associated viremia in NiV experimentally infected animals and suggested a progressive infection that seeded multiple organs simultaneously from the initial site of virus replication. Unlike the cat, hamster and squirrel monkey models of NiV infection, severe respiratory pathology, neurological disease and generalized vasculitis all manifested in NiV-infected AGMs, providing an accurate reflection of what is observed in NiV-infected humans. Our findings demonstrate the first consistent and highly pathogenic NHP model of NiV infection, providing a new and critical platform in the evaluation and licensure of either passive and active immunization or therapeutic strategies for human use

Identification and Characterization of Lineage-Specific Highly Conserved Noncoding Sequences in Mammalian Genomes

Vertebrate genome comparisons revealed that there are highly conserved noncoding sequences (HCNSs) among a wide range of species and many of which contain regulatory elements. However, recently emerged sequences conserved in specific lineages have not been well studied. Toward this end, we identified 8,198 primate and 21,128 specific HCNSs as representative ones among mammals from human–marmoset and mouse–rat comparisons, respectively. Derived allele frequency analysis of primate-specific HCNSs showed that these HCNSs were under purifying selection, indicating that they may harbor important functions. We selected the top 1,000 largest HCNSs and compared the lineage-specific HCNS-flanking genes (LHF genes) with ultraconserved element (UCE)-flanking genes. Interestingly, the majority of LHF genes were different from UCE-flanking genes. This lineage-specific set of LHF genes was more enriched in protein-binding function. Conversely, the number of LHF genes that were also shared by UCEs was small but significantly larger than random expectation, and many of these genes were involved in anatomical development as transcriptional regulators, suggesting that certain groups of genes preferentially recruit new HCNSs in addition to old HCNSs that are conserved among vertebrates. This group of LHF genes might be involved in the various levels of lineage-specific evolution among vertebrates, mammals, primates, and rodents. If so, the emergence of HCNSs in and around these two groups of LHF genes developed lineage-specific characteristics. Our results provide new insight into lineage-specific evolution through interactions between HCNSs and their LHF genes

Turtle Carapace Anomalies: The Roles of Genetic Diversity and Environment

Background: Phenotypic anomalies are common in wild populations and multiple genetic, biotic and abiotic factors might contribute to their formation. Turtles are excellent models for the study of developmental instability because anomalies are easily detected in the form of malformations, additions, or reductions in the number of scutes or scales. Methodology/Principal Findings: In this study, we integrated field observations, manipulative experiments, and climatic and genetic approaches to investigate the origin of carapace scute anomalies across Iberian populations of the European pond turtle, Emys orbicularis. The proportion of anomalous individuals varied from 3 % to 69 % in local populations, with increasing frequency of anomalies in northern regions. We found no significant effect of climatic and soil moisture, or climatic temperature on the occurrence of anomalies. However, lower genetic diversity and inbreeding were good predictors of the prevalence of scute anomalies among populations. Both decreasing genetic diversity and increasing proportion of anomalous individuals in northern parts of the Iberian distribution may be linked to recolonization events from the Southern Pleistocene refugium. Conclusions/Significance: Overall, our results suggest that developmental instability in turtle carapace formation might be caused, at least in part, by genetic factors, although the influence of environmental factors affecting the developmental stability of turtle carapace cannot be ruled out. Further studies of the effects of environmental factors, pollutants an