Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.

Background Classical aphasiology, based on the study of stroke sequelae, fuses speech fluency and grammatical ability. Nonfluent (Broca's) aphasia often is accompanied by agrammatism; whereas in the fluent aphasias grammatical deficits are not typical. The assumption that a similar relationship exists in primary progressive aphasia (PPA) has led to the dichotomization of this syndrome into fluent and nonfluent subtypes. Aims This study compared elements of fluency and grammatical production in the narrative speech of individuals with PPA to determine if they can be dissociated from one another. Method Speech samples from 37 individuals with PPA, clinically assigned to agrammatic (N=11), logopenic (N=20) and semantic (N=6) subtypes, and 13 cognitively healthy control participants telling the “Cinderella Story” were analyzed for fluency (i.e., words per minute (WPM) and mean length of utterance in words (MLU-W)) and grammaticality (i.e., the proportion of grammatically correct sentences, open-to-closed-class word ratio, noun-to-verb ratio, and correct production of verb inflection, noun morphology, and verb argument structure.) Between group differences were analyzed for each variable. Correlational analyses examined the relation between WPM and each grammatical variable, and an off-line measure of sentence production. Outcomes And Results Agrammatic and logopenic groups both had lower scores on the fluency measures and produced significantly fewer grammatical sentences than did semantic and control groups. However, only the agrammatic group evinced significantly impaired production of verb inflection and verb argument structure. In addition, some semantic participants showed abnormal open-to-closed and noun-to-verb ratios in narrative speech. When the sample was divided on the basis of fluency, all the agrammatic participants fell in the nonfluent category. The logopenic participants varied in fluency but those with low fluency showed variable performance on measures of grammaticality. Correlational analyses and scatter plots comparing fluency and each grammatical variable revealed dissociations within PPA participants, with some nonfluent participants showing normal grammatical skill. Conclusions Grammatical production is a complex construct comprised of correct usage of several language components, each of which can be selectively affected by disease. This study demonstrates that individuals with PPA show dissociations between fluency and grammatical production in narrative speech. Grammatical ability, and its relationship to fluency, varies from individual to individual, and from one variant of PPA to another, and can even be found in individuals with semantic PPA in whom a fluent aphasia is usually thought to accompany preserved ability to produce grammatical utterances.

Abstract. The paper reports findings derived from three experiments examining syntactic and morphosyntactic processing in individuals with agrammatic and logopenic variants of primary progressive aphasia (PPA-G and PPA-L, respectively) and strokeinduced agrammatic and anomic aphasia (StrAg and StrAn, respectively). We examined comprehension and production of canonical and noncanonical sentence structures and production of tensed and nontensed verb forms using constrained tasks in experiments 1 and 2, using the Northwestern Assessment of Verbs and Sentences (NAVS [57]) and the Northwestern Assessment of Verb Inflection (NAVI, Thompson and Lee, experimental version) test batteries, respectively. Experiment 3 examined free narrative samples, focusing on syntactic and morphosyntactic measures, i.e. production of grammatical sentences, noun to verb ratio, open-class to closed-class word production ratio, and the production of correctly inflected verbs. Results indicate that the two agrammatic groups (i.e., PPA-G and StrAg) pattern alike on syntactic and morphosyntactic measures, showing more impaired noncanonical compared to canonical sentence comprehension and production and greater difficulties producing tensed compared to nontensed verb forms. Their spontaneous speech also contained significantly fewer grammatical sentences and correctly inflected verbs, and they produced a greater proportion of nouns compared to verbs, than healthy speakers. In contrast, PPA-L and StrAn individuals did not display these deficits, and performed significantly better than the agrammatic groups on these measures. The findings suggest that agrammatism, whether induced by degenerative disease or stroke, is associated with characteristic deficits in syntactic and morphosyntactic processing. We therefore recommend that linguistically sophisticated tests and narrative analysis procedures be used to systematically evaluate the linguistic ability of individuals with PPA, contributing to our understanding of the language impairments of different PPA variants.

At present, a number of computer vision-based crack detection techniques have been developed to efficiently inspect and manage a large number of structures. However, these techniques have not replaced visual inspection, as they have been developed under near-ideal conditions and not in an on-site environment. This article proposes an automated detection technique for crack morphology on concrete surface under an on-site environment based on convolutional neural networks (CNNs). A well-known CNN, AlexNet is trained for crack detection with images scraped from the Internet. The training set is divided into five classes involving cracks, intact surfaces, two types of similar patterns of cracks, and plants. A comparative study evaluates the successfulness of the detailed surface categorization. A probability map is developed using a softmax layer value to add robustness to sliding window detection and a parametric study was carried out to determine its threshold. The applicability of the proposed method is evaluated on images taken from the field and real-time video frames taken using an unmanned aerial vehicle. The evaluation results confirm the high adoptability of the proposed method for crack inspection in an on-site environment.

Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%.

BACKGROUND/OBJECTIVESFeeding in infancy is the most significant determinant of the intestinal microbiota in early life. The aim of this study was to determine the gut microbiota of Korean infants and compare the microbiota obtained between breast-fed and formula-fed Korean infants.SUBJECTS/METHODSWe analyzed the microbial communities in fecal samples collected from twenty 4-week old Korean (ten samples in each breast-fed or formula-fed) infants using pyrosequencing.RESULTSThe fecal microbiota of the 4-week-old Korean infants consisted of the three phyla Actinobacteria, Firmicutes, and Proteobacteria. In addition, five species, including Bifidocbacterium longum, Streptococcus salivarius, Strepotococcus lactarius, Streptococcus pseudopneumoniae, and Lactobacillus gasseri were common commensal intestinal microbiota in all infants. The predominant intestinal microbiota in the breast-fed infants (BFI) included the phylum Actinobacteria (average 70.55%), family Bifidobacteriacea (70.12%), genus Bifidobacterium (70.03%) and species Bifidobacterium longum (69.96%). In the microbiota from the formula-fed infants (FFI), the proportion of the phylum Actinobacteria (40.68%) was less, whereas the proportions of Firmicutes (45.38%) and Proteobacteria (13.85%) as well as the diversity of each taxonomic level were greater, compared to those of the BFI. The probiotic species found in the 4-week-old Korean infants were Bifidobacterium longum, Streptococcus salivarius, and Lactobacillus gasseri. These probiotic species accounted for 93.81% of the microbiota from the BFI, while only 63.80% of the microbiota from the FFI. In particular, B. longum was more abundant in BFI (69.96%) than in FFI (34.17%).CONCLUSIONSBreast milk supports the growth of B. longum and inhibits others. To the best of our knowledge, this study was the first attempt to analyze the gut microbiota of healthy Korean infants according to the feeding type using pyrosequencing. Our data can be used as a basis for further studies to investigate the development of intestinal microbiota with aging and disease status.

No abstract

We have previously reported that bone marrow cells (BMCs) participate in the regeneration after liver injury. However, it is not established that this is the result of differentiation of hematopoietic stem cells (HSCs), mesenchymal stem cells (MSCs) or the combination of both. We investigated the contribution of each cell fraction to the regenerative process. First, we confirmed that transplanted stem cells migrate directly to injured liver tissue without dispersing to other organs. Next, we divided green fluorescent protein (GFP)-expressing BMCs into three populations as mononuclear cells, MSCs and HSCs. We then compared the engraftment capacity after transplantation of each fraction of cells into liver-injured mice. Of these, the MSCs transplanted group showed the highest GFP fluorescence intensities in liver tissue by flow cytometry analysis and confocal microscopic observation. Furthermore, MSCs showed differentiation potential into hepatocytes when co-cultured with injured liver cells, which suggests that MSCs showed highest potential for the regeneration of injured liver tissue compared with those of the other two cell refractions.