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Infrared thermography provides key information for a wide range of diagnostic applications within built and natural environments. As thermal states are changing with ambient conditions, it is important to deploy thermal imaging systems and operators opportunistically. It is therefore an attractive proposition to make these systems more affordable and accessible. Low-cost thermal sensors generally produce low-resolution outputs. To increase data density across large subjects, diagnosticians may create image mosaics from multiple overlapping thermographs. The registration of individual inputs into large mosaics is aided by the acquisition of additional sensor data (photographs and depthmaps), which can provide critical spatial references. In many cases, the materials inherent to the modern built environment present challenges to traditional data registration workflows between multiple sensor streams. Mobile devices offer an opportunity to innovate in the creation of these mosaics, integrating rapid geospatial mapping functionality with radiometric thermography within a 3D context. In this paper the authors evaluate the FLIR One Pro thermal camera module along with iOS/iPhone specific rapid mapping capabilities, and present a methodology: (1) introducing a workflow for the integration of short-range (within 0.3–5 m capture distance) iPhone mobile sensor data into modeling pipelines; (2) introducing a calibration model enabling effective registration and fusion of multi-modal inputs from the iPhone mobile sensor stack and FLIR One thermographic module; and (3) detailing an alternative open-source methodology for the evaluation and translation of thermographic imagery for multi-sensor fusion. The end product of this pipeline is a 3D radiometric thermographic mosaic: a spatially continuous, textured surface model in which hundreds of individual low-resolution thermographs are fused into a single queryable output retaining full 16-bit temperature values at every point. All datasets have been made openly available and the two case studies used in this paper have been made accessible at full resolution for interactive 3D online viewing.

Hepcidin is a hormone secreted in response to iron loading and inflammation. Hepcidin binds to the iron exporter ferroportin, inducing its degradation and thus preventing iron entry into plasma. We determined that hepcidin binding to ferroportin leads to the binding and activation of the protein Janus Kinase2 (Jak2), which is required for phosphorylation of ferroportin. Ferroportin is a dimer and both monomers must be capable of binding hepcidin for Jak2 to bind to ferroportin. Once Jak2 is bound to the ferroportin dimer, both ferroportin monomers must be functionally competent to activate Jak2 and for ferroportin to be phosphorylated. These results show that cooperativity between the ferroportin monomers is required for hepcidin-mediated Jak2 activation and ferroportin down-regulation. These results provide a molecular explanation for the dominant inheritance of hepcidin resistant iron overload disease.

Unmanned aerial vehicle (UAV) vision-based sensing has become an emerging technology for structural health monitoring (SHM) and post-disaster damage assessment of civil infrastructure. This article proposes a framework for monitoring structural displacement under earthquakes by reprojecting image points obtained courtesy of UAV-captured videos to the 3-D world space based on the world-to-image point correspondences. To identify optimal features in the UAV imagery, geo-reference targets with various patterns were installed on a test building specimen, which was then subjected to earthquake shaking. A feature point tracking-based algorithm for square checkerboard patterns and a Hough Transform-based algorithm for concentric circular patterns are developed to ensure reliable detection and tracking of image features. Photogrammetry techniques are applied to reconstruct the 3-D world points and extract structural displacements. The proposed methodology is validated by monitoring the displacements of a full-scale 6-story mass timber building during a series of shake table tests. Reasonable accuracy is achieved in that the overall root-mean-square errors of the tracking results are at the millimeter level compared to ground truth measurements from analog sensors. Insights on optimal features for monitoring structural dynamic response are discussed based on statistical analysis of the error characteristics for the various reference target patterns used to track the structural displacements.

Purpose: A speech-specific reinvestment scale (SSRS) is a psychometric measure of the propensity to consciously control and monitor speech production. This study develops and validates an SSRS as well as examines its relationship with speech performance with the moderating effects of trait social anxieties (i.e., social interaction anxiety, public speaking anxiety, and social phobia). Method: Scale development involves the following stages: (a) initial item generation based on relevant literature, (b) item evaluation through cognitive interviews with 24 healthy respondents, (c) scale reliability and validity tests using cross-sectional survey data from 498 healthy respondents, and (d) test--retest reliability assessment using longitudinal survey data from 185 healthy respondents. Respondents' speech performance is quantified using speech examination scores. Hierarchical moderated regression analyses are conducted to examine the moderating effects of trait social anxieties. Results: The validated SSRS comprises 35 items, which can be categorized into four subdimensions, namely, speech movement self-consciousness, public consciousness of speech content, speech manner, and speech movement. Results show that respondents with low trait social anxieties indicate a generally positive relationship between public consciousness of speech movement and speech performance, whereas respondents with high trait social anxieties exhibit a nonsignificant relationship. Conclusions: SSRS offers a reliable and valid method for assessing the predisposition for conscious speech control and monitoring, which plays a role in speech performance and is moderated by an individual's level of trait social anxiety. SSRS is a potential assessment tool for speech-language pathologists to evaluate the impacts of conscious speech control and monitoring on individuals with speech impairment.

To facilitate second language learning, it has become increasingly popular to use a second language as the medium of instruction for content subjects for majority language students. Although numerous research studies have shown the advantages of such kind of programs in North America and Europe, those investigating English as the Medium of Instruction (EMI) schools in Hong Kong yielded inconclusive results. This meta-analysis is the first attempt to synthesize the research evidence on EMI education in Hong Kong since 1970. Based on 24 studies, this meta-analysis shows that students in EMI secondary schools were more proficient in second language and performed better on measures of affective variables. Yet their learning in other content subjects suffered. The differences between the effectiveness of EMI education in Hong Kong and that of similar programs in other contexts will be discussed, thereby illuminating second language acquisition theories and bilingual education.

Background A genomic signal track is a set of genomic intervals associated with values of various types, such as measurements from high-throughput experiments. Analysis of signal tracks requires complex computational methods, which often make the analysts focus too much on the detailed computational steps rather than on their biological questions. Results Here we propose Signal Track Query Language (STQL) for simple analysis of signal tracks. It is a Structured Query Language (SQL)-like declarative language, which means one only specifies what computations need to be done but not how these computations are to be carried out. STQL provides a rich set of constructs for manipulating genomic intervals and their values. To run STQL queries, we have developed the Signal Track Analytical Research Tool (START, http://yiplab.cse.cuhk.edu.hk/start/ ), a system that includes a Web-based user interface and a back-end execution system. The user interface helps users select data from our database of around 10,000 commonly-used public signal tracks, manage their own tracks, and construct, store and share STQL queries. The back-end system automatically translates STQL queries into optimized low-level programs and runs them on a computer cluster in parallel. We use STQL to perform 14 representative analytical tasks. By repeating these analyses using bedtools, Galaxy and custom Python scripts, we show that the STQL solution is usually the simplest, and the parallel execution achieves significant speed-up with large data files. Finally, we describe how a biologist with minimal formal training in computer programming self-learned STQL to analyze DNA methylation data we produced from 60 pairs of hepatocellular carcinoma (HCC) samples. Conclusions Overall, STQL and START provide a generic way for analyzing a large number of genomic signal tracks in parallel easily.

Purpose This research aims to examine the effects of error experience when learning to speak with lowered nasalance level. Method A total of 45 typical speakers were instructed to learn to lower speech nasalance level in either an errorless (restricted possibility for committing errors) or an errorful (unrestricted possibility for committing errors) learning condition. The nasality level of the participants' speech was measured by a nasometer and quantified by nasalance scores (in percent). Errorless learners practiced producing speech with lowered nasalance level with a threshold nasalance score of 50% (the easiest target) at the beginning, which gradually decreased to a threshold of 10% (the most difficult target) at the end. The same set of threshold targets was presented to errorful learners, but in reverse order. Errors were defined by the proportion of speech, with a nasalance score exceeding the threshold. Retention and transfer tests were administered. Results Errorless learners displayed fewer errors and lower mean nasalance scores than errorful learners during the acquisition phase. Furthermore, errorless learners achieved lower mean nasalance scores than errorful learners in the retention and transfer tests. Conclusion These results suggest that errorless learning is more effective than errorful learning and that error experience has a detrimental effect on the acquisition of a novel speech motor task that requires minimization of the nasality level. Errorless learning may be a useful paradigm for the intervention and management of hypernasality in clinical settings where behavioral treatments are needed.

BackgroundDespite the advances of cardiac surgery in the last decades, the incidence of acute kidney insufficiency (AKI) post isolated coronary artery bypass grafting surgery (iCABG) is increasing. The purpose of this study was to identify the potential predictors of postoperative AKI in iCABG in order to implement measures to reduce its incidence.ResultsWe reviewed the data of 1914 patients who had on-pump iCABG from January 2008 to December 2015, using the cardiothoracic surgery unit database of Liverpool Hospital, Sydney, New South Wales, Australia. Patients were divided into two groups according to the occurrence of postoperative AKI. The incidence of postoperative AKI was 5.3% in 8 years (n = 101/1914 patients). Multivariable analysis has identified the following independent predictors of postoperative AKI; age older than 80 years (OR 4.27; 95% CI 1.6–11.81) (p < 0.005), diabetes mellitus (OR 1.96; 95% CI 1.073–3.65) (p = 0.034), peripheral vascular disease (OR 2.55; 95% CI 1.16–5.59) (p < 0.01), severe congestive cardiac symptoms (i.e., NYHA III & IV) (OR 1.9; 95% CI 1.26–2.92) (p = 0.002), surgical re-exploration (OR 2.49; 95% CI 1.07–5.76) (p = 0.04), postoperative red blood cell (RBC) transfusion (OR 4.93; 95% CI 2.25–10.80) (p < 0.0001), and postoperative low cardiac output syndrome (OR 2.85; 95% CI 1.49–5.46) (p < 0.0001).ConclusionPostoperative AKI after iCABG is a complex problem. Predictors of postoperative AKI can be mitigated by accurate risk-based assessment and intraoperative proper surgical hemostasis.

This twin survey of online journalism professionals and students examines their perceptions of journalism skills, duties, and concepts. Using samples of online journalists and journalism students in Hong Kong, Asia’s media hub, it attempts to offer updated insights into the changes taking place in journalism classrooms and newsrooms and uncovers the discordance between online journalism education and practice. The study finds that traditional journalistic skills remain prioritized over technical skills in online newsrooms. The findings also suggest that today’s journalism students are fairly proficient in new media skills. Therefore, journalism curricula should not forgo training students in traditional journalistic skills for computer skills.