Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Review Protocol to Assess the Effectiveness of Usability Questionnaires in mHealth App Studies

Background Usability questionnaires have a wide use in mobile health (mHealth) app usability studies. However, no systematic review has been conducted for assessing the effectiveness of these questionnaires. Objective This paper describes a protocol for conducting a systematic review of published questionnaire-based mHealth app usability studies. Methods In this systematic review, we will select recently published (2008-2017) articles from peer-reviewed journals and conferences that describe mHealth app usability studies and implement at least one usability questionnaire. The search strategy will include terms such as “mobile app” and “usability. ” Multiple databases such as PubMed, CINAHL, IEEE Xplore, ACM Digital Library, and INSPEC will be searched. There will be 2 independent reviewers in charge of screening titles and abstracts as well as determining those articles that should be included for a full-text review. The third reviewer will act as a mediator between the other 2 reviewers. Moreover, a data extraction form will be created and used during the full article data analysis. Notably, the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines will be followed in reporting this protocol. Results A preliminary search produced 1271 articles, 40 of which are duplicate records. The inclusion-exclusion criteria are being strictly followed in performing the ongoing study selection. Conclusions Usability questionnaires are an important tool in mHealth app usability studies. This review will summarize the usability questionnaires used in published research articles while assessing the efficacy of these questionnaires in determining the usability of mHealth apps.
Urinalysis for the diagnosis of glomerulonephritis: role of dysmorphic red blood cells

Background Dysmorphic red blood cells (dRBCs) on urine microscopy have been associated with glomerulonephritis (GN). We assessed the prevalence and ability of dRBCs to differentiate GN from other kidney diseases. Methods Adult patients with kidney biopsy performed between 2012 and 2015 at a single center who had a concurrent urinalysis were retrospectively studied. The association of ≥25% dRBCs with the presence of glomerular pathology was assessed. Univariate and multivariate logistic regression were performed on significantly associated variables. Results The mean age of the 482 eligible subjects was 55 years and 47.7% were female. Overall, 173 (35.9%) had <25% and 76 (15.8%) had ≥25% urine dRBCs. Kidney biopsies revealed glomerular disease in 372 (77.2%) (GN 46% and non-GN 54%). At the dRBC threshold of ≥25% used at our center, a sensitivity of 20.4%, specificity of 96.3% and positive predictive value of 94.6% for glomerular disease were observed. In a logistic regression model, urine RBCs [>10 versus ≤10 (P < 0.001)] but not dRBCs ≥25% (P = 0.3) independently predicted the presence of GN. A scoring system (0-3) based on hematuria and proteinuria levels revealed the risk for biopsy-proven GN was 15% when the score was 0 compared with 83% when it was 3. Conclusions The presence of ≥25% urine dRBCs is specific but not sensitive for GN. In this cohort, the combined hematuria (>10 RBCs/high-power field) and proteinuria performed just as well as dRBCs plus proteinuria to predict underlying GN. A model based on the degree of hematuria and proteinuria found on urinalysis was able to predict the presence of GN.
25 Measuring Interaction Proxemics with Wearable Light Tags

ALESSANDRO MONTANARI∗, Computer Laboratory, University of Cambridge ZHAO TIAN∗, Department of Computer Science, Dartmouth College ELENA FRANCU, School of Business and Economics, Maastricht University BENJAMIN LUCAS, Nottingham University Business School, University of Nottingham BRIAN JONES, Computer Laboratory, University of Cambridge XIA ZHOU, Department of Computer Science, Dartmouth College CECILIA MASCOLO, Computer Laboratory, University of Cambridge
Modeling and Analysis of Thermal Effects in Optical Networks-on-Chip

The performance of multiprocessor systems, such as chip multiprocessors (CMPs), is determined not only by the performance of their processors, but also by how efficiently they collaborate with one another. It is the communication architectures which determine the collaboration efficiency on the hardware side. Optical networks-on-chip (ONoCs) are emerging communication architectures that can potentially offer ultra-high communication bandwidth and low latency to multiprocessor systems. Thermal sensitivity is an intrinsic characteristic of photonic devices used by ONoCs as well as a potential issue. For the first time, this paper systematically modeled and quantitatively analyzed the thermal effects in ONoCs and their impacts. We presented an analytical ONoC thermal model, and show that on-chip temperature fluctuations can dramatically reduce the worst-case ONoC power efficiency. For instance, the power efficiency of an ONoC will drop to about 5pJ/bit when chip temperature reaches 85oC. We revealed three important factors regarding ONoC power efficiency under temperature variations, and found the optimal initial device conditions to minimize thermal impacts. The impacts of the thermal effects on the ONoC power consumption under different chip temperatures, device characteristics, and ONoC configurations are quantitatively analyzed. The findings in this paper can help support the further development of this emerging technology.
Securing Cooperative Spectrum Sensing Against Collusive SSDF Attack using XOR Distance Analysis in Cognitive Radio Networks

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, if CSS assumes that all secondary users (SU) are honest, it may offer opportunities for attackers to conduct a spectrum sensing data falsification (SSDF) attack. To suppress such a threat, recent efforts have been made to develop trust mechanisms. Currently, some attackers can collude with each other to form a collusive clique, and thus not only increase the power of SSDF attack but also avoid the detection of a trust mechanism. Noting the duality of sensing data, we propose a defense scheme called XDA from the perspective of XOR distance analysis to suppress a collusive SSDF attack. In the XDA scheme, the XOR distance calculation in line with the type of “0” and “1” historical sensing data is used to measure the similarity between any two SUs. Noting that collusive SSDF attackers hold high trust value and the minimum XOR distance, the algorithm to detect collusive SSDF attackers is designed. Meanwhile, the XDA scheme can perfect the trust mechanism to correct collusive SSDF attackers’ trust value. Simulation results show that the XDA scheme can enhance the accuracy of trust evaluation, and thus successfully reduce the power of collusive SSDF attack against CSS.
Friends or foes? Emerging insights from fungal interactions with plants

Fungi interact with plants in various ways, with each interaction giving rise to different alterations in both partners. While fungal pathogens have detrimental effects on plant physiology, mutualistic fungi augment host defence responses to pathogens and/or improve plant nutrient uptake. Tropic growth towards plant roots or stomata, mediated by chemical and topographical signals, has been described for several fungi, with evidence of species-specific signals and sensing mechanisms. Fungal partners secrete bioactive molecules such as small peptide effectors, enzymes and secondary metabolites which facilitate colonization and contribute to both symbiotic and pathogenic relationships. There has been tremendous advancement in fungal molecular biology, omics sciences and microscopy in recent years, opening up new possibilities for the identification of key molecular mechanisms in plant–fungal interactions, the power of which is often borne out in their combination. Our fragmentary knowledge on the interactions between plants and fungi must be made whole to understand the potential of fungi in preventing plant diseases, improving plant productivity and understanding ecosystem stability. Here, we review innovative methods and the associated new insights into plant–fungal interactions.
On the Surface Roughness in Cylindrical Plunge Grinding

The effects of the following factors on the surface roughness in cylindrical plunge grinding are analysed theoretically by a statistical approach: (1) The wheel depth of cut. (2) The ratio of the grinding wheel to the workpiece in number of revolutions. (3) The thread on the grinding wheel surface caused by dressing treatment. Furthermore, the surface roughness in cylindrical traverse grinding is analysed theoretically by a statistical approach. And experimental results are compared with the theoretical values calculated by use of the measured distributions of the following factors: (1) The position of grain cutting edges in the radial direction on the grinding wheel surface layer. (2) The tip angle of grain cutting edges.
A Bayesian framework for molecular strain identification from mixed diagnostic samples

We provide a mathematical formulation and develop a computational framework for identifying multiple strains of microorganisms from mixed samples of DNA. Our method is applicable in public health domains where efficient identification of pathogens is paramount, e.g. for the monitoring of disease outbreaks. We formulate strain identification as an inverse problem that aims at simultaneously estimating a binary matrix (encoding presence or absence of mutations in each strain) and a real-valued vector (representing the mixture of strains) such that their product is approximately equal to the measured data vector. The problem at hand has a similar structure to blind deconvolution, except for the presence of binary constraints, which we enforce in our approach. Following a Bayesian approach, we derive a posterior density. We present two computational methods for solving the non-convex maximum a posteriori estimation problem. The first one is a local optimization method that is made efficient and scalable by decoupling the problem into smaller independent subproblems, whereas the second one yields a global minimizer by converting the problem into a convex mixed-integer quadratic programming problem. The decoupling approach also provides an efficient way to integrate over the posterior. This provides useful information about the ambiguity of the underdetermined problem and, thus, the uncertainty associated with numerical solutions. We evaluate the potential and limitations of our framework in silico using synthetic and experimental data with available ground truths.
ESA‐UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives

Motivation: Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry‐based methods. Existing prediction methods use randomly selected non‐validated sites as non‐ubiquitination sites to train ubiquitination site prediction models. Results: We propose an evolutionary screening algorithm (ESA) to select effective negatives among non‐validated sites and an ESA‐based prediction method, ESA‐UbiSite, to identify human ubiquitination sites. The ESA selects non‐validated sites least likely to be ubiquitination sites as training negatives. Moreover, the ESA and ESA‐UbiSite use a set of well‐selected physicochemical properties together with a support vector machine for accurate prediction. Experimental results show that ESA‐UbiSite with effective negatives achieved 0.92 test accuracy and a Matthews's correlation coefficient of 0.48, better than existing prediction methods. The ESA increased ESA‐UbiSite's test accuracy from 0.75 to 0.92 and can improve other post‐translational modification site prediction methods. Availability and Implementation: An ESA‐UbiSite‐based web server has been established at http://iclab.life. nctu.edu.tw/iclab_webtools/ESAUbiSite/. Contact: syho@mail.nctu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online.
Enhanced guide-RNA Design and Targeting Analysis for Precise CRISPR Genome Editing of Single and Consortia of Industrially Relevant and Non-Model Organisms

Motivation Genetic diversity of non-model organisms offers a repertoire of unique phenotypic features for exploration and cultivation for synthetic biology and metabolic engineering applications. To realize this enormous potential, it is critical to have an efficient genome editing tool for rapid strain engineering of these organisms to perform novel programmed functions. Results To accommodate the use of CRISPR/Cas systems for genome editing across organisms, we have developed a novel method, named CASPER (CRISPR Associated Software for Pathway Engineering and Research), for identifying on- and off-targets with enhanced predictability coupled with an analysis of non-unique (repeated) targets to assist in editing any organism with various endonucleases. Utilizing CASPER, we demonstrated a modest 2.4% and significant 30.2% improvement (F-test, p<0.05) over the conventional methods for predicting on- and off-target activities, respectively. Further we used CASPER to develop novel applications in genome editing: multitargeting analysis (i.e. simultaneous multiple-site modification on a target genome with a sole guide-RNA (gRNA) requirement) and multispecies population analysis (i.e. gRNA design for genome editing across a consortium of organisms). Our analysis on a selection of industrially relevant organisms revealed a number of non-unique target sites associated with genes and transposable elements that can be used as potential sites for multitargeting. The analysis also identified shared and unshared targets that enable genome editing of single or multiple genomes in a consortium of interest. We envision CASPER as a useful platform to enhance the precise CRISPR genome editing for metabolic engineering and synthetic biology applications.
Impact of hydrophobic treatment of jute on moisture regain and mechanical properties of composite material

This study was aimed to investigate the effect of fluorocarbon, hydrocarbon, and hybrid fluorocarbon on the mechanical properties and moisture regain of jute fiber reinforced composite materials. A significant difference in moisture regain values of treated and untreated reinforcement samples was observed, when the concentration of chemical finish (hybrid fluorocarbs) was 40 g/L. The composite made from treated reinforcement regained very low moisture content as well as exhibited improved mechanical properties (tensile and flexural strength). Having both characteristics (hydrophilic and hydrophobic groups) in hybrid fluorocarbon, the treated jute fibers and corresponding composites showed better properties as compared to counterparts treated by other two chemicals, due to better interface.
Cumulative Exposure to Systolic Blood Pressure During Young Adulthood Through Midlife and the Urine Albumin-to-Creatinine Ratio at Midlife

BACKGROUND Higher blood pressure during young adulthood may increase cardiovascular and kidney disease risk later in life. This study examined the association of cumulative systolic blood pressure (SBP) exposure during young adulthood through midlife with urine albumin-to-creatinine ratios (ACR) measured during midlife. METHODS We used data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a biracial cohort recruited in 4 urban areas during years 1985-1986. Cumulative SBP was calculated as the average SBP between 2 exams multiplied by years between exams over 20 year years. ACR was measured 20 years after baseline when participants were age 43-50 years (midlife). A generalized additive model was used to examine the association of log ACR as a function of cumulative SBP with adjustment for covariates including SBP measured concurrently with ACR. RESULTS Cumulative SBP ranged from a low of 1,671 to a high of 3,260 mm Hg. Participants in the highest cumulative SBP quartile were more likely to be male (61.4% vs. 20.7%; P < 0.001), Black (61.5% vs. 25.6%; P < 0.001) and have elevated ACR (18.7% vs. 4.8%; P < 0.001) vs. lowest quartile. Spline regression curves of ACR vs. cumulative SBP demonstrated an inflection point in ACR with cumulative SBP levels >2,350 mm Hg with linear increases in ACR above this threshold. Adjusted geometric mean ACR values were significantly higher with cumulative SBP ≥2,500 vs. <2500 (9.18 [1.06] vs. 6.92 [1.02]; P < 0.0001). CONCLUSION Higher SBP during young adulthood through midlife is associated with higher ACR during midlife.
Contrasting evolutionary processes during Quaternary climatic changes and historical orogenies: a case study of the Japanese endemic primroses Primula sect. Reinii

Background and Aims Recent biogeographic studies have illustrated that Quaternary climatic changes and historical orogenies have driven the development of high levels of biodiversity. In this context, phylogenetic niche conservatism may play a role as a major precursor of allopatric speciation. However, the effects of niche evolution on the diversification patterns of plant species under rapid habitat changes are still unknown. Here, Primula section Reinii, one of the few primroses endemic to the Japanese Archipelago, was investigated. This study aimed to clarify the phylogenetic position and relationships of section Reinii, interpret the biogeographic and diversification patterns of this group and gain a better understanding of the role of climatic niche evolution in the Japanese endemic primroses. Methods Dated phylogeny for Primula section Reinii is presented based on the sequences of six chloroplast genes and one nuclear gene. Biogeographic history was reconstructed using statistical dispersal-vicariance analysis. Macro-evolutionary modelling of the climatic niche was combined with biogeographic inferences. Key Results Section Reinii was shown to be monophyletic based on chloroplast and nuclear sequences. Fossil-calibrated dating analysis estimated that this section diverged from its sister taxon, sect. Cortusoides, around 1.82 million years ago, and intraspecific diversification occurred within the last million years. This time frame was characterized by Quaternary climatic oscillations and uplift of high mountains in Japan. Biogeographic inference suggested that this section originated at the northern end of the Japanese Archipelago and then dispersed southward to other islands. Models of climatic niche evolution indicated that the closely related species P. reinii and P. tosanensis have contrasting niche optima and rates of niche evolution. Conclusion Our results highlight that spatiotemporal heterogeneity in the Japanese islands may play a significant role in the biogeographic history of Japanese endemic primroses. Contrasting evolutionary processes found in closely related species illustrate the effects of climatic niche evolution on species' diversification patterns.
Improving Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) Treatment Monitoring in South Africa: Evaluation of an Advanced TB/HIV Course for Healthcare Workers

Abstract Background South Africa has dual epidemics of human immunodeficiency virus (HIV) and tuberculosis (TB). Nurse-focused training was combined with onsite mentoring for nurses to improve HIV and TB care. A pre-/postevaluation was conducted in 3 districts in South Africa to assess the effects of the course on clinical patient monitoring and integration of TB and HIV care. Methods Two cross-sectional, unmatched samples of patient charts at 76 primary healthcare facilities were collected retrospectively in 2014 to evaluate the impact of training on treatment monitoring. Proportions of HIV patients receiving a viral load test 6 months after initiating antiretroviral therapy (ART) and TB patients receiving end of intensive phase sputum testing were compared pre- and posttraining. Analysis of creatinine clearance testing and integration of TB and HIV care were also performed. Results Data were analyzed from 1074 pretraining and 1048 posttraining records among patients initiating ART and from 1063 pretraining and 1008 posttraining among patients initiating TB treatment. Documentation of a 6-month viral load test was 36.3%, and a TB test at end of intensive phase was 70.7%, and neither increased after training. Among patients with a viral load test, the percentage with viral load less than 50 copies/mL increased from 48.6% pretraining compared with 64.2% posttraining (P = .001). Integration of TB and HIV care such as isoniazid preventive therapy increased significantly. Conclusions The primary outcome measures did not change after training. However, the evaluation documented many other improvements in TB and HIV care that may have been supported by the course.
Statistical Analysis of the Effects of Heavyweight and Lightweight Methodologies on the Six-Pointed Star Model

Traditionally, software development organizations relied on heavyweight development methodologies, such as waterfall, V-model, and others. Later, agile development methodologies known as lightweight methodologies were introduced. Many considered these to be more flexible and more effective than heavyweight methodologies. Both methodologies are equally important for a software development life cycle. The purpose of adopting software development methodologies is to optimize the process model to achieve milestones while concurrently and effectively managing time, budget, and quality. The literature review reveals that there is a lack of statistical evidence for determining the effect of both methodologies on the six-pointed star model (schedule, scope, budget, risk, resource, and quality). In this paper, statistical comparisons were performed for the effects of both methodologies on each factor of the six-pointed star model and the interdependency among factors. Numerical analyses were conducted based on survey responses collected from the experienced users of both methodologies. After examining the results of all the factors of both methodologies, it was determined that lightweight methodologies are suitable for small-scale projects and that heavyweight methodologies perform better for medium- and large-scale projects.
A simulation study on implementing marginal structural models in an observational study with switching medication based on a biomarker

ABSTRACT Assessing treatment effectiveness in longitudinal data can be complex when treatments are not randomly assigned and patients are allowed to switch treatment to other or no treatment, often in a manner that is driven by changes in one or more variables associated with patient or clinical characteristics. There can be confounding of the treatment effect from a time-varying variable, i.e., one which is affected by previous exposure and can in turn also influence subsequent treatment changes. Precision medicine relies on validated biomarkers to better classify patients by their probable response to treatment. However, biomarkers may be a source of time-varying confounding, which are affected by prior treatment in the evaluation and are also subject to measurement errors. The impact of switching medications based on a biomarker has received less attention. We conducted simulation studies to explore biased estimation under various scenarios when marginal structural model estimations are employed. Holding model misspecification issues constant, bias is severe in the presence of multiple switching, along with measurement error and missing data in the covariates.