Software engineering is concerned with how best to create software in ways that promote sustainable development and maximise quality. We have been largely successful at transferring software engineering knowledge into the industry, however, many challenges in software engineering training remain. A key amongst these is how best to teach practical engineering approaches along with the theoretical concepts behind them. This paper describes our experience of adopting an agile approach for reflective learning and teaching within the context of our Software Systems Engineering module, aimed at addressing challenges identified with previous efforts to promote reflective practice. Our study attempts to strengthen the use of reflective learning approaches for our current cohort, as well as introducing reflective teaching practices, whereby we examine our teaching approach in order to improve its efficiency and effectiveness. Our analysis of student response to the module shows that it was very well-received by the students, and we were able to collect ample evidence from feedback to support this. Most of our approaches resulted in positive feedback and contributed to improvements in teaching quality, however, we also identified some key aspects in our method that could still benefit from refinement, such as the need for explicit links between learning outcomes and workshop activities, and intuitive design of feedback questions, along with feedback collection frequency. We plan to incorporate these additional updates into the revision of the module for the next academic year, and to continue collecting and analysing feedback data for further enhancement.

OBJECTIVE:Few data are available on whether changes in metabolic syndrome affect incident gout. This study was undertaken to assess associations between metabolic syndrome status and incident gout, as well as changes in the clinical characteristics of metabolic syndrome and incident gout, in a cohort of young men.METHODS:This nationwide, population-based cohort study included 20-39-year-old men who participated in serial health check-ups. The outcome, incident gout, was defined according to the claims database diagnostic code for gout. Associations among changes in metabolic syndrome status and incident gout were analyzed using Cox proportional hazards models.RESULTS:Among 1,293,166 individuals, 18,473 were diagnosed as having gout (incidence rate 3.36 per 1,000 person-years). Subjects who had chronic metabolic syndrome (defined as metabolic syndrome at all 3 health check-ups) had a nearly 4-fold higher risk of incident gout compared to subjects who did not have metabolic syndrome at any of the 3 health check-ups (adjusted hazard ratio [HRadj ] 3.82 [95% confidence interval (95% CI) 3.67-3.98]). Development of metabolic syndrome more than doubled the risk of incident gout (HRadj 2.31 [95% CI 2.20-2.43]). Conversely, recovery from metabolic syndrome reduced the risk of incident gout by nearly half (HRadj 0.52 [95% CI 0.49-0.56]). Among metabolic syndrome components, changes in elevated triglycerides (development of elevated triglycerides, HRadj 1.74 [95% CI 1.66-1.81]; recovery from elevated triglycerides, HRadj 0.56 [95% CI 0.54-0.59]) and abdominal obesity (development of abdominal obesity, HRadj 1.94 [95% CI 1.85-2.03]; recovery from abdominal obesity, HRadj 0.69 [95% CI 0.64-0.74]) showed the greatest association with altered risk of incident gout. Associations between changes in the status and clinical characteristics of metabolic syndrome and incident gout were more pronounced in subjects ages 20-29 years compared to those ages 30-39 years, and in subjects who were underweight or who had a normal weight.CONCLUSION:Changes in the status and clinical characteristics of metabolic syndrome were associated with altered risk of incident gout. These results suggest that metabolic syndrome is a modifiable risk factor for gout.

Background: Radiographs are widely used to evaluate radiographic progression with modified stoke ankylosing spondylitis spinal score (mSASSS). Objective: This pilot study aimed to develop a deep learning model for grading the corners of the cervical and lumbar vertebral bodies for computer-aided detection of mSASSS in patients with ankylosing spondylitis (AS). Methods: Digital radiographic examination of the spine was performed using Discovery XR656 (GE Healthcare) and Digital Diagnost (Philips). The disk points were detected between the bodies using a key-point detection deep learning model from the image obtained in DICOM (digital imaging and communications in medicine) format from the cervical and lumbar spinal radiographs. After cropping the vertebral regions around the disk point, the lower and upper corners of the vertebral bodies were classified as grade 3 (total bony bridges) or grades 0, 1, or 2 (non-bridges). We trained a convolutional neural network model to predict the grades in the lower and upper corners of the vertebral bodies. The performance of the model was evaluated in a validation set, which was separate from the training set. Results: Among 1280 patients with AS for whom mSASSS data were available, 5,083 cervical and 5245 lumbar lateral radiographs were reviewed. The total number of corners where mSASSS was measured in the cervical and lumbar vertebrae, including the upper and lower corners, was 119,414. Among them, the number of corners in the training and validation sets was 110,088 and 9326, respectively. The mean accuracy, sensitivity, and specificity for mSASSS scoring in one corner of the vertebral body were 0.91604, 0.80288, and 0.94244, respectively. Conclusion: A high-performance deep learning model for grading the corners of the vertebral bodies was developed for the first time. This model must be improved and further validated to develop a computer-aided tool for assessing mSASSS in the future.

Most of previous works and applications of Bayesian factor model have assumed the normal likelihood regardless of its validity. We propose a Bayesian factor model for heavy-tailed high-dimensional data based on multivariate Student- t likelihood to obtain better covariance estimation. We use multiplicative gamma process shrinkage prior and factor number adaptation scheme proposed in Bhattacharya and Dunson [Biometrika 98(2):291–306, 2011]. Since a naive Gibbs sampler for the proposed model suffers from slow mixing, we propose a Markov Chain Monte Carlo algorithm where fast mixing of Hamiltonian Monte Carlo is exploited for some parameters in the proposed model. Simulation results illustrate the gain in performance of covariance estimation for heavy-tailed high-dimensional data. We also provide a theoretical result that the posterior of the proposed model is weakly consistent under reasonable conditions. We conclude the paper with the application of the proposed factor model on breast cancer metastasis prediction given DNA signature data of cancer cells.

The eruptive young star V899 Mon shows characteristics of both FUors and EXors. It reached a peak brightness in 2010, then briefly faded in 2011, followed by a second outburst. We conducted multifilter optical photometric monitoring, as well as optical and near-infrared spectroscopic observations, of V899 Mon. The light curves and color-magnitude diagrams show that V899 Mon has been gradually fading after its second outburst peak in 2018, but smaller accretion bursts are still happening. Our spectroscopic observations taken with Gemini/IGRINS and VLT/MUSE show a number of emission lines, unlike during the outbursting stage. We used the emission line fluxes to estimate the accretion rate and found that it has significantly decreased compared to the outbursting stage. The mass-loss rate is also weakening. Our 2D spectroastrometric analysis of emission lines recovered jet and disk emission of V899 Mon. We found that the emission from permitted metallic lines and the CO bandheads can be modeled well with a disk in Keplerian rotation, which also gives a tight constraint for the dynamical stellar mass of 2 M (circle dot). After a discussion of the physical changes that led to the changes in the observed properties of V899 Mon, we suggest that this object is finishing its second outburst.

Software product line engineering is a paradigm for developing a family of software products from a repository of reusable assets rather than developing each individual product from scratch. In featureoriented software product line engineering, the common and the variable characteristics of the products are expressed in terms of features. Using software product line engineering approach, software products are produced en masse by means of two engineering phases: (i) Domain Engineering and, (ii) Application Engineering. At the domain engineering phase, reusable assets are developed with variation points where variant features may be bound for each of the diverse products. At the application engineering phase, individual and customized products are developed from the reusable assets. Ideally, the reusable assets should be adaptable with less effort to support additional variations (features) that were not planned beforehand in order to increase the usage context of SPL as a result of expanding markets or when a new usage context of software product line emerges. This paper presents an exploration research to investigate the properties of features, in the code-asset implemented using Object-Oriented Programming Style. In the exploration, we observed that program elements of disparate features formed unions as well as intersections that may affect modifiability of the code-assets. The implication of this research to practice is that an unstable product line and with the tendency of emerging variations should aim for techniques that limit the number of intersections between program elements of different features. Similarly, the implication of the observation to research is that there should be subsequent investigations using multiple case studies in different software domains and programming styles to improve the understanding of the findings.

We present fundamental parameters for 110 canonical K- & M-type (1.3$-$0.13$M_\odot$) Taurus-Auriga young stellar objects (YSOs). The analysis produces a simultaneous determination of effective temperature ($T_{\rm eff}$), surface gravity ($\log$ g), magnetic field strength (B), and projected rotational velocity ($v \sin i$). Our method employed synthetic spectra and high-resolution (R$\sim$45,000) near-infrared spectra taken with the Immersion GRating INfrared Spectrometer (IGRINS) to fit specific K-band spectral regions most sensitive to those parameters. The use of these high-resolution spectra reduces the influence of distance uncertainties, reddening, and non-photospheric continuum emission on the parameter determinations. The median total (fit + systematic) uncertainties were 170 K, 0.28 dex, 0.60 kG, 2.5 km s$^{-1}$ for $T_{\rm eff}$, $\log$ g, B, and $v \sin i$, respectively. We determined B for 41 Taurus YSOs (upper limits for the remainder) and find systematic offsets (lower $T_{\rm eff}$, higher $\log$ g and $v \sin i$) in parameters when B is measurable but not considered in the fit. The average $\log$ g for the Class II and Class III objects differs by 0.23$\pm$0.05dex, which is consistent with Class III objects being the more evolved members of the star-forming region. However, the dispersion in $\log$ g is greater than the uncertainties, which highlights how the YSO classification correlates with age ($\log$ g), yet there are exceptionally young (lower $\log$ g) Class III YSOs and relatively old (higher $\log$ g) Class II YSOs with unexplained evolutionary histories. The spectra from this work are provided in an online repository along with TW Hydrae Association (TWA) comparison objects and the model grid used in our analysis.

We have developed compact implicit finite-difference (FD) schemes in the time-space domain based on the second-order FD approximation for accurate solution of the acoustic wave equation in 1D, 2D, and 3D. Our method is based on the weighted linear combination of the second-order FD operators with different spatial orientations to mitigate numerical error anisotropy and the weighted averaging of the mass acceleration term over the grid points of the second-order FD stencil to reduce the overall numerical dispersion error. We have developed a derivation of the schemes for 1D, 2D, and 3D cases. We obtain their corresponding dispersion equations, then we find the optimum weights by optimization of the time-space domain dispersion function, and finally we tabulate the optimized weights for each case. We analyze the numerical dispersion, stability, and convergence rates of our schemes and compare their numerical dispersion characteristics with the standard high-order ones. We also discuss the efficient solution of the system of equations associated with our implicit schemes using the conjugate-gradient method. The comparison of dispersion curves and the numerical solutions with the analytical and the pseudospectral solutions reveals that our schemes have better performance than the standard spatial high-order schemes and remain stable for relatively large time steps.

Weissella koreensis (W. koreensis) DB1 with high-ornithine-producing capacity was isolated from kimchi and shows the anti-adipogenic activity via its production of ornithine. This study was carried out the effects of W. koreensis DB1 cell extracts on the inhibition of adipogenesis in 3T3-L1 cells. MTT results showed that 0-0.75 μg/mL W. koreensis DB1 cell extracts had no significant effect on cell vability. The cell extracts of W. koreensis DB1 at non-cytotoxic concentrations significantly suppressed the differentiated 3T3-L1 cells by decreasing triglyceride level and intracellular lipid accumulation compared to that associated with the untreated control group. The major transcriptional factors involved in adipocyte differentiation, such as peroxisome proliferator-activated receptorγ (PPARγ), CCAAT/enhancer-binding proteinα (C/EBPα), and sterol regulatory element-binding transcription factor-1c (SREBP-1c), were down-regulated by the cell extracts of W. koreensis DB1 treatments compared with the untreated control group. Furthermore, the expressions of lipogenesis-related enzymes including acetyl-CoA carboxylase (ACC) and fatty acid synthase (FAS) genes were also decreased by the cell extracts of W. koreensis DB1 treatment. Accordingly, these results indicate that the cell extracts of W. koreensis DB1 treatment inhibited the adipogenesis and lipogenesis of 3T3-L1 cells, suggesting a protective role in adipocyte differentiation.

OBJECTIVE:To identify the prevalence and characteristics of neuropathic pain (NP) in patients with spinal cord injury (SCI) and to investigate associations between NP and demographic or disease-related variables.METHODS:We retrospectively reviewed medical records of patients with SCI whose pain was classified according to the International Spinal Cord Injury Pain classifications at a single hospital. Multiple statistical analyses were employed. Patients aged <19 years, and patients with other neurological disorders and congenital conditions were excluded.RESULTS:Of 366 patients, 253 patients (69.1%) with SCI had NP. Patients who were married or had traumatic injury or depressive mood had a higher prevalence rate. When other variables were controlled, marital status and depressive mood were found to be predictors of NP. There was no association between the prevalence of NP and other demographic or clinical variables. The mean Numeric Rating Scale (NRS) of NP was 4.52, and patients mainly described pain as tingling, squeezing, and painful cold. Females and those with below-level NP reported more intense pain. An NRS cut-off value of 4.5 was determined as the most appropriate value to discriminate between patients taking pain medication and those who did not.CONCLUSION:In total, 69.1% of patients with SCI complained of NP, indicating that NP was a major complication. Treatment planning for patients with SCI and NP should consider that marital status, mood, sex, and pain subtype may affect NP, which should be actively managed in patients with an NRS ≥4.5.

Digital health technology utilizing wearables, IoT and mobile devices has been successfully applied in the monitoring of numerous diseases and conditions. However, intervention, in response to monitored data, is yet to benefit from technological support and continues to follow a traditional point-of-care delivery model by providers and health professionals. Mental health is an example of a critical health area in dire need for technology solutions to enable timely, effective and scalable interventions. This is especially the case with an increasing prevalence of mental health conditions and a declining capacity of the healthcare professional workforce. Numerous studies reveal the potential for peer support groups as an effective, scalable, cost-effective, first-line of response in mental health interventions. Peer support helps participants, at low and moderate risk, better understand their diseases or conditions and empowers them to take control of their own health. Peer support interactions also seems to inform health professionals with insights and intricate knowledge, making it effectively a learning health system. This paper proposes a software architecture to better enable "peer-sourcing". We present related work and show how the proposed architecture might draw similarity to and differences from crowd-sourcing architectures. We also present a study in which we interacted with service users (mental health patients) and mental healthcare professionals to better understand and elicit the key requirements for the software architecture.

In this study, we introduce a staggered time integrator with the Fourier pseudospectral method to solve the first-order linear wave equation, which is accelerated by using the Jacobi-Anger expansion. The proposed method can reduce the computational cost by approximately half compared to the scheme whose temporal order of accuracy is extended by the Lax-Wendroff method under the equivalent modeling conditions, such as time step length, grid interval and maximum wave propagation speed. This is because the Jacobi-Anger expansion can effectively approximate the sinusoidal function, which in our case is the sine function in the wavenumber domain. Based on the wavenumber domain analysis of the proposed method, a strategy to optimally design the simulation parameters to maintain a preset level of accuracy is also introduced. According to the strategy, as the time step length increases, not only the computational cost of the simulation is reduced, but also the accuracy of the numerical solution is improved. Numerical simulations are also performed by using both homogeneous and heterogeneous models to validate the introduced strategy, which supports the practicality of the proposed method.

As the Internet of Things (IoT) proliferates, the potential for its opportunistic interaction with traditional mobile apps becomes apparent. We argue that to fully take advantage of this potential, mobile apps must become things themselves, and interact in a smart space like their hardware counterparts. We present an extension to our Atlas thing architecture on smartphones, allowing mobile apps to behave as things and provide powerful services and functionalities. To this end, we also consider the role of the mobile app developer, and introduce actionable keywords (AKWs)---a dynamically programmable description---to enable potential thing to thing interactions. The AKWs empower the mobile app to dynamically react to services provided by other things, without being known a priori by the original app developer. In this paper, we present the mobile-apps-as-things (MAAT) concept along with its AKW concept and programming construct. For MAAT to be adopted by developers, changes to the existing development environments (IDE) should remain minimal to stay acceptable and practically usable, thus we also propose an IDE plugin to simplify the addition of this dynamic behavior. We present details of MAAT, along with the implementation of the IDE plugin, and give a detailed benchmarking evaluation to assess the responsiveness of our implementation to impromptu interactions and dynamic app behavioral changes. We also investigate another study, targeting Android developers, which evaluates the acceptability and usability of the MAAT IDE plugin.

We propose a method to design a discrete fracture network (DFN) model for seismic wave simulations that eliminates complex meshing requirements and poor mesh design. An ant-tracking volume is generated from the original seismic to capture the global fracture geometry. A mesh structure is specified prior to adding fractures. Fractures are then specified on the mesh in the form of small fracture patches following the trend of ant-tracking signals in the previously defined mesh. In the proposed method, the implementation of the DFN model is thus independent of the mesh structure. Also, fracture clusters in the final DFN model are composed of multiple small patches, and each small patch is represented by the linear slip model in the form of a discontinuity of the displacement field. We also performed wave simulations to validate the proposed method of DFN model building, and investigated the wave behavior in the given fracture structure. For efficient implementation of wave modeling, we employed the generalized multiscale finite element method to discretize the elastic wave equation with explicit fractures. The wave simulation results show that a small fracture segment could be ignored when the wavelength is long enough; however, fracture clusters can induce large displacement discontinuities and wave scattering, and these effects become obvious as we apply high source frequency.

Krull, Kimberly R. Sokal, Gregory N. Mace, Larissa Nofi, L. Prato, Jae-Joon Lee, and Daniel T. Jaffe Department of Physics & Astronomy, Rice University, 6100 Main St., Houston TX 77005, USA, Department of Astronomy, The University of Texas at Austin, Austin, TX 78712, USA, IfA, University of Hawaii, 2680 Woodlawn Dr., Honolulu, HI 96822, USA, Lowell Observatory, 1400 Mars Hill Road, Flagstaff, AZ 86001, USA, KASI, 776 Daedeokdae-ro, Yuseong-gu, Daejeon 34055, Korea.

AbstractThe aim of this study was to investigate the ability of Lactobacillus plantarum EM as a starter culture to control cabbage–apple juice fermentation and to explore the cholesterol‐lowering effects of the fermented juice (EM juice) in rats. L. plantarum EM produced strong antimicrobial activities against bacteria and fungi, suppressing other microorganisms in the fermented juice, and was the dominant organism during fermentation and storage. The EM juice also showed strong and broad‐spectrum antimicrobial activity. Rats fed a high‐fat and high‐cholesterol diet and administered EM juice showed significantly reduced total cholesterol (TC), triglyceride, and LDL‐cholesterol levels, as well as a reduced atherogenic index, lower cardiac factors in serum, and lower TC levels in the liver, while total lipid and TC levels in the rat feces increased. Reverse transcription–polymerase chain reaction analysis revealed that the hepatic mRNA expression of HMG‐CoA reductase decreased and the expressions of cholesterol 7α‐hydroxylase and low‐density lipoprotein receptor increased in rats administered EM juice. The effects of EM juice on rats included inhibition of cholesterol synthesis as well as enhancement of cholesterol uptake and cholesterol excretion. The results of this study indicate that the use of L. plantarum EM as a functional starter culture for juice fermentation exerts microbial control, enhances sanitary safety, and provides beneficial food effects against hypercholesterolemia.

Objective. To evaluate the fate of abstracts presented at scientific meetings of the Korean College of Rheumatology (KCR). Methods. This study examined the abstracts presented at annual meetings of the KCR from 2005 to 2014. Only original studies were selected, excluding case reports. A manual search was conducted using PubMed, KoreaMed, Cochrane Library, and Embase to track the published articles. The abstracts were considered to have been published if the authors, title, study design, and results were the same for a published article. In addition, they were considered published if the author and the study design matched, even if the results of the abstract and the results of the published articles were not identical. Results. A total of 928 abstracts from 2005 to 2014 were analyzed. Of the 928 abstracts, 468 (50.43%) abstracts were published in a peer-reviewed journal and the mean time to publication was 19 months. Of the 468 abstracts, 414 were published in a science citation index extended (SCI[E]) journal, and 54 were published in non-SCI(E) journals. The proportion of SCI(E) articles increased annually. The average impact factor for the SCI(E) journals was 2.93. In subgroup analysis, the abstracts that were awarded the best oral or best poster presentation were more likely to be published as full-length articles with a higher impact factor than the abstracts not awarded. Conclusion. Half of the abstracts presented in the KCR annual meetings were published in a peer-reviewed journal. Approximately 90% of the articles were published in a SCI(E) journal.