Extant Methods Research Articles

FlopR: An Open Source Software Package for Calibration and Normalization of Plate Reader and Flow Cytometry Data.

The measurement of gene expression using fluorescence markers has been a cornerstone of synthetic biology for the past two decades. However, the use of arbitrary units has limited the usefulness of these data for many quantitative purposes. Calibration of fluorescence measurements from flow cytometry and plate reader spectrophotometry has been implemented previously, but the tools are disjointed. Here we pull together, and in some cases improve, extant methods into a single software tool, written as a package in the R statistical framework. The workflow is validated using Escherichia coli engineered to express green fluorescent protein (GFP) from a set of commonly used constitutive promoters. We then demonstrate the package’s power by identifying the time evolution of distinct subpopulations of bacteria from bulk plate reader data, a task previously reliant on laborious flow cytometry or colony counting experiments. Along with standardized parts and experimental methods, the development and dissemination of usable tools for quantitative measurement and data analysis will benefit the synthetic biology community by improving interoperability.

WarpClothingOut: A Stepwise Framework for Clothes Translation From the Human Body to Tiled Images

With the increasing popularity of online shopping, searching for products with images for item retrieval has gradually become an effective approach. This trend is especially evident in the fashion industry. In common media, clothing items are usually worn on the human body. They can be straightforwardly segmented from the source media by utilizing detection or parsing algorithms. However, this may be deleterious to retrieval performance due to distortion, occlusion, and different backgrounds. In this article, a stepwise translation framework using generative adversarial network and thin plate spline is developed to transfer human body images to tiled clothing images, which can be directly used for clothing retrieval. Experimental results demonstrate the effectiveness of the resultant tiled images produced from our framework in comparison to other extant methods.

Femoral histomorphometric age-at-death studies: The issue of sample size and standard error.

Extant histomorphometric aging methods based on the analysis of the femoral cortex generally report small samples (N<100) and highly variable standard error of the estimate (SEE) values (±1.51‒16.98 years). The present paper reviews the published literature on femoral histomorphometry for age-at-death estimation in order to examine the relationship between sample size and SEE values, and makes recommendations for minimum reporting requirements for age-at-death studies based on statistical data. The SEE from a total of 33 studies are analysed. Sample size and confidence intervals are explored using Hennig and Cooper's simulation modelling. Analysis of effect size through a fixed-effect model is performed on 5/33 studies to examine the relationship between sample size and effect size. The pooled sex formulae from Nor etal., Martrille etal. and Thompson and the two sex-specific formulae of Pfeiffer are examined, as they report mean and standard deviation values for both chronological and estimated ages. The results of these analyses support sampling theory, specifically wide variation in SEE when N<100, narrowing as the sample size increases, and lower effect sizes in the larger of the five studies examined. The findings provide some support for a minimum threshold of 100‒150 individuals for histomorphometric age-at-death estimation. Analysis of effect size is suggested for future investigation in meta-analyses of forensic anthropological age-estimation studies. To ensure increased precision and meaningful comparison, large samples should be used for histomorphometry, and authors should report SEE and discrete statistics (e.g. n, mean, standard deviation) for both chronological age and estimated age.

Isotopic Meshing of a Real Algebraic Space Curve

This paper presents a new algorithm for computing the topology of an algebraic space curve. Based on an efficient weak generic position-checking method and a method for solving bivariate polynomial systems, the authors give a first deterministic and efficient algorithm to compute the topology of an algebraic space curve. Compared to extant methods, the new algorithm is efficient for two reasons. The bit size of the coefficients appearing in the sheared polynomials are greatly improved. The other is that one projection is enough for most general cases in the new algorithm. After the topology of an algebraic space curve is given, the authors also provide an isotopic-meshing (approximation) of the space curve. Moreover, an approximation of the algebraic space curve can be generated automatically if the approximations of two projected plane curves are first computed. This is also an advantage of our method. Many non-trivial experiments show the efficiency of the algorithm.

Bayesian Synthetic Control Methods

The authors propose a new Bayesian synthetic control framework to overcome limitations of extant synthetic control methods (SCMs). The proposed Bayesian synthetic control methods (BSCMs) do not impose any restrictive constraints on the parameter space a priori. Moreover, they provide statistical inference in a straightforward manner as well as a natural mechanism to deal with the “large p, small n” and sparsity problems through Markov chain Monte Carlo procedures. Using simulations, the authors find that for a variety of data-generating processes, the proposed BSCMs almost always provide better predictive accuracy and parameter precision than extant SCMs. They demonstrate an application of the proposed BSCMs to a real-world context of a tax imposed on soda sales in Washington state in 2010. As in the simulations, the proposed models outperform extant models, as measured by predictive accuracy in the posttreatment periods. The authors find that the tax led to an increase of 5.7% in retail price and a decrease of 5.5%∼5.8% in sales. They also find that retailers in Washington overshifted the tax to consumers, leading to a pass-through rate of approximately 121%.

Identifying Regulatory Elements via Deep Learning

Deep neural networks have been revolutionizing the field of machine learning for the past several years. They have been applied with great success in many domains of the biomedical data sciences and are outperforming extant methods by a large margin. The ability of deep neural networks to pick up local image features and model the interactions between them makes them highly applicable to regulatory genomics. Instead of an image, the networks analyze DNA and RNA sequences and additional epigenomic data. In this review, we survey the successes of deep learning in the field of regulatory genomics. We first describe the fundamental building blocks of deep neural networks, popular architectures used in regulatory genomics, and their training process on molecular sequence data. We then review several key methods in different gene regulation domains. We start with the pioneering method DeepBind and its successors, which were developed to predict protein–DNA binding. We then review methods developed to predict and model epigenetic information, such as histone marks and nucleosome occupancy. Following epigenomics, we review methods to predict protein–RNA binding with its unique challenge of incorporating RNA structure information. Finally, we provide our overall view of the strengths and weaknesses of deep neural networks and prospects for future developments.

Complex interval-valued intuitionistic fuzzy TODIM approach and its application to group decision making

Present work proposes novel fuzzy information based TODIM approaches that can deal with the evaluations under complex interval-valued intuitionistic fuzzy (CIVIF) environment. The proposed approaches have been referred to as complex interval-valued intuitionistic fuzzy-TODIM (CIVIF-TODIM) approaches. The proposed method encompasses the characteristic features of a complex intuitionistic fuzzy set, interval-valued fuzzy set, and TODIM methodology. At first, the definitions associated with CIVIF have been discussed and then the methodological steps involved in classical TODIM have been delineated. The classical TODIM approach is then extended to deal with group decision-making problems under the CIVIF environment. Robustness, effectiveness, applicability, and the improvements made to the extant fuzzy TODIM methods by the proposed methodology have been adjudged through the consideration of illustrative examples solved by the past researcher. Sensitivity analysis with respect to the attenuation factor as well as the criteria weights has been provided to justify the robustness of the proposed methods. A comparative analysis with the existing fuzzy-TODIM approaches has been delineated and a comprehensive analysis of the ranking results obtained for different distance measures at each value of attenuation factor is provided towards the end of the work. The carried out inclusive analysis on the approaches that have been proposed in the present work reveals that the proposed CIVIF fuzzy TODIM approaches are superior to the existing fuzzy TODIM methods. Therefore, the present study provides its contribution to the domain of decision-making framework through the approaches that provide for dealing with complex, uncertain, and linguistic information in an efficient manner.

A Review Selection Method for Finding an Informative Subset from Online Reviews

Concerning the information overload of online reviews, this paper models a new review selection problem called the Informative Review Subset Selection problem (namely, IRSS) and demonstrates that it is NP-hard to solve and approximate. Furthermore, a novel heuristic method (namely, Combined Search-ComS) is proposed for seeking the solution to the problem and selecting a subset of reviews, which is consistent with the original review corpus in light of mutual information entropy. The proposed method is then comprehensively examined via extensive data experiments and a user study on Amazon data. Experimental results reveal the overall superiority of the proposed method in comparison with other extant methods of concern, showing that it is an effective way to select an informative subset of online reviews. The proposed method is deemed desirable and useful for online consumers and service providers.

Linguistic group decision making: Axiomatic distance and minimum cost consensus

The axiomatic distance-based method is a powerful tool to aggregate individual preferences, and the extant axiomatic distance-based aggregation methods are with regard to individual numerical preferences. However, in some real-world decision problems with qualitative aspects, it is more convenient and natural for individuals to express their preferences through linguistic terms rather than through numerical values. Therefore, in this paper, we propose axiomatic distance in the linguistic context based on ordered linguistic term sets to aggregate individual linguistic preferences. Specifically, we provide some natural axioms on the distance measure among linguistic preferences. We then prove that there exists a unique distance function that satisfies all the proposed axioms. Based on the axiomatic distance function, we aggregate individual linguistic preferences into the group linguistic preference, which minimizes the total distance among individual linguistic preferences. Furthermore, we present a novel consensus measure based on the unique axiomatic distance and develop a minimum cost consensus model to obtain the optimal adjusted linguistic preference, which serves as a reference for the moderator to persuade individuals to modify their linguistic preferences.

Navigating through the complex transport system: A heuristic approach for city tourism recommendation

The fast development of machine learning and artificial intelligence has led to a great improvement of the smart tourism recommendation system, however many problems associated with the choice of transport modes in city tourism have yet to be solved. This research attempts to address this issue by proposing a model of customized day itineraries with consideration of transport mode choice. With improved particle swarm optimization and differential evolution algorithm, a nondominated sorting heuristic approach was devised. A case study was carried out in Chengdu, China to examine the performance of our approach. The results show that compared with extant methods, our approach achieves better performance. In addition, our approach can create more sensible, multifarious, and customized itineraries than previous methods. Tourism organizations and mobile map app providers could integrate our proposed model into their existing smart service systems, as part of their e-business or digital strategy for enhancing tourist experience.

The generalized inference on the ratio of mean differences for fraction retention noninferiority hypothesis.

The fraction retention non-inferiority hypothesis is often measured for the ratio of the effects of a new treatment to those of the control in medical research. However, the fraction retention non-inferiority test that the new treatment maintains the efficacy of control can be affected by the nuisance parameters. Herein, a heuristic procedure for testing the fraction retention non-inferiority hypothesis is proposed based on the generalized p-value (GPV) under normality assumption and heteroskedasticity. Through the simulation study, it is demonstrated that, the performance of the GPV-based method not only adequately controls the type I error rate at the nominal level but also is uniformly more powerful than the ratio test, Rothmann’s and Wang’s tests, the comparable extant methods. Finally, we illustrate the proposed method by employing a real example.

Abstracts from the SCT 40th Annual Meeting (2019)

Purpose: Many clinical trials of promising interventions fail to see anticipated effects at follow-up. To avoid carrying out a full trial when the tested treatment is likely to be inefficacious, researchers may include one or more interim analyses, assessing for trial futility before the final analysis, discontinuing the trial early if interim effects are less than expected. The current approach tests whether the trial is likely to show futility, not whether the treatment is likely to be inefficacious. Treatments that show efficacy but with wide confidence bounds around efficacy estimates may be stopped inappropriately for futility. We present an alternative approach which focuses on the expected efficacy of the treatment. The approach uses “futility regions” to determine whether to stop trials of treatments that are unlikely to show a clinically important effect at interim—a stopping rule which requires interim interval estimates to lie entirely within a “stopping zone” (current approaches only require estimates to cross into the region of null/futile effects). We contrast this with extant methods, highlighting how the proposed approach focuses on a clinically relevant question, and protects against undue stopping because of imprecise estimates. For a researcher wanting to gather evidence of efficacious treatments, this is a desirable characteristic; trials with precise interim estimates of null effects should be stopped, whereas those with imprecise estimates that include useful effect magnitudes should be allowed to continue. Method: We created a simulation study, testing 1000 permutations of six different parallel-design trial scenarios, all featuring one interim analysis, and stopping only for futility. The scenarios demonstrate a range of effect sizes and variances. We implemented four types of interval-based stopping rules—using Frequentist and Bayesian approaches—and compared their performance to the more commonly used O’Brien-Fleming and Pocock designs. We used a range of stopping rules, 15 in total, to demonstrate the limits of the approaches in the different trial scenarios. Results: All approaches discriminated well between trials with a final null effect, and those with a final large treatment effect. However, when the trial variance was different to that expected at the design stage, the interval-based approaches demonstrated favorable characteristics—continuing more permutations with imprecise but promising estimates, and stopping more of those with precise estimates of clinically trivial or null (futile) effect sizes (table). Empirical Bayesian approaches showed enhanced precision compared to the other methods, due to their property of allowing interim estimates to inform the prior distributions of the final effect estimate. Conclusion: A simple implementation of adaptive trial methodology, featuring only one interim analysis and using a modified version of interval estimate stopping rules demonstrated preferable performance characteristics for the researcher seeking to stop only trials that precisely estimate null effects than more current approaches. These characteristics have the benefit of producing two possible meaningful trial endpoints: completed trials that had continued to show promise at interim, and stopped trials with precise estimates of a null effect. This contrasts with a trial which stops early with an imprecise estimate of effect, about which few conclusions can be drawn.

MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.

To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is needed. In contrast, extant predictive methods do not comprehensively address these aspects of drug discovery and rely on features derived from extensive, often unavailable experimental information for novel molecules. To address these issues, we developed MEDICASCY, a multilabel-based boosted random forest machine learning method that only requires the small molecule's chemical structure for the drug side effect, indication, efficacy, and probable mode of action target predictions; however, it has comparable or even significantly better performance than existing approaches requiring far more information. In retrospective benchmarking on high confidence predictions, MEDICASCY shows about 78% precision and recall for predicting at least one severe side effect and 72% precision drug efficacy. Experimental validation of MEDICASCY's efficacy predictions on novel molecules shows close to 80% precision for the inhibition of growth in ovarian, breast, and prostate cancer cell lines. Thus, MEDICASCY should improve the success rate for new drug approval. A web service for academic users is available at http://pwp.gatech.edu/cssb/MEDICASCY.

Analysis of Combined Pile Raft Foundations Based on a Static Load Test

Combined pile raft foundations (CPRF) take into account the synergy of a raft with a group of piles in transferring the load to the ground. In many ground conditions, such foundations are optimal in both utilitarian and economic terms. Based on a literature review, it can be concluded that, despite the numerous extant calculation methods for the analysis of piled raft foundations, there is lack of research related to the possibility of using a load-settlement curve from a static pile test in the full range, in order to analyse the load-settlement curve of a pile working in a group under the raft. Furthermore, the current trend in the design of pile foundations, based on the static load test databases, encourages the formulation of methods using the results of the static pile test. These methods allow for analyses closer to the physical model. In this paper, the above research postulate was answered, formulating a mathematical model in which a particular emphasis was placed on the actual work of a single pile in the ground, taking into account the interaction among raft, piles and subsoil. The developed mathematical model was verified on the basis of the results of field research. For this purpose, the results of settlement of two tanks were used.

Data-driven outreach to opportunity youth using population data and Geographic Information System technology

Summary Opportunity youth (i.e. ages 16–24 who are neither working nor in school) are difficult to engage in services. Population-level data and Geographic Information System (GIS) technology play a critical role in helping social work practitioners understand where target populations reside and which characteristics are most salient to their service needs. This case study of the Opportunities for Youth Coalition demonstrates the use of population-level data and GIS technology in social work research and practice. We describe the collective impact approach, extant methods of youth outreach, and the Coalition’s efforts to address youth disconnection. We used American Community Survey data, ArcGIS software, and hot spot analysis to answer three research questions. Findings In the Phoenix Metro Area, there are 75,200 opportunity youth. Greater percentages of youth of color are opportunity youth in comparison with White youth. One in three individuals who have limited English proficiency and who have a disability are opportunity youth. Nearly one in five individuals living in poverty is an opportunity youth. We identify the top five high population density areas for opportunity youth. Further, we highlight three ways that the Coalition’s use of data and GIS technology has enhanced youths’ and service providers’ engagement: (1) increased referrals, (2) increased involvement of organizations, and (3) increased understanding of special populations. Applications We discuss the benefits and challenges of using population data and GIS technology to guide youth engagement. We conclude with strategies for community practitioners to replicate in other geographic areas and with other hard-to-reach populations.

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

Models and methods for determining the optimal number of beds in hospitals and regions: a systematic scoping review

BackgroundDetermining the optimal number of hospital beds is a complex and challenging endeavor and requires models and techniques which are sensitive to the multi-level, uncertain, and dynamic variables involved. This study identifies and characterizes extant models and methods that can be used to determine the required number of beds at hospital and regional levels, comparing their advantages and challenges.MethodsA systematic search was conducted using Web of Science, Scopus, Embase and PubMed databases, with the search terms hospital bed capacity, hospital bed need, hospital, bed size, model, and method.ResultsTwenty-three studies met the criteria to be included in the review. Of these studies, a total of 11 models and 5 methods were identified, mainly designed to determine hospital bed capacity at the regional level. Common determinants of the required number of hospital beds in these models included demographic changes, average length of stay, admission rates, and bed occupancy rates.ConclusionsThere are no specific norms for the required number of beds at hospital and regional levels, but some of the identified models and methods may be used to estimate this number in different contexts. Moreover, it is important to consider alternative approaches to planning hospital capacity like care pathways to fix the limitations of “bed numbers”.

Development of RT-qPCR assays for the detection of three latent viruses of pome

Latent fruit tree viruses present economic threat to the industry and nurseries as diseases they cause not only reduce fruit quality and production yield, but can also be spread inadvertently through propagation due to the lack of viral symptoms on an infected mother plant. As a result, these viruses require appropriate detection tools for effective management. In this study we developed RT-qPCR assays for the detection of three latent viruses of pome, apple chlorotic leaf spot virus (ACLSV), apple stem pitting virus (ASPV), and apple mosaic virus (ApMV), using the alignment of representative sequences from the NCBI database. The optimized assays were shown to be specific by successfully amplifying the target from positive controls without showing any detectable amplification in negative and non-target controls, and revealed high sensitivity by reliably detecting as low as 101 copies per reaction. The results also demonstrated that both the choice of extraction method and the reagents used for RT-qPCRcould play a critical role in virus detection outcome. These assays were both reliable and robust compared to the extant RT-PCR methods, and they could be a viable tool for making informed management decisions.

Beyond the Unit Root Question: Uncertainty and Inference

AbstractA fundamental challenge facing applied time‐series analysts is how to draw inferences about long‐run relationships (LRR) when we are uncertain whether the data contain unit roots. Unit root tests are notoriously unreliable and often leave analysts uncertain, but popular extant methods hinge on correct classification. Webb, Linn, and Lebo (WLL; 2019) develop a framework for inference based on critical value bounds for hypothesis tests on the long‐run multiplier (LRM) that eschews unit root tests and incorporates the uncertainty inherent in identifying the dynamic properties of the data into inferences about LRRs. We show how the WLL bounds procedure can be applied to any fully specified regression model to solve this fundamental challenge, extend the results of WLL by presenting a general set of critical value bounds to be used in applied work, and demonstrate the empirical relevance of the LRM bounds procedure in two applications.

Camera Unavoidable Scene Watermarks: A Method for Forcibly Conveying Information onto Photographs

When a scene’s image rights need to be protected e.g. a stage performance, it is valuable to use human imperceptible methods to forcibly add markers to a camera’s perception of the scene regardless of the camera’s precise location, focus distance, or shutter speed. This work expands upon extant methods for adding human imperceptible, camera perceptible markers to scenes but does so with the assumption that the photographer will take natural steps to avoid capturing the markers. The proposed method utilizes a combination of a traditional method of adding an image to the scene, and projections from the scene onto the camera’s entrance pupil. This method is intended to function even when the target camera utilizes an IR filter and has a shutter speed 1/60 s. More interestingly, the combination of traditional images with projecting onto the camera allows this method to not be reliant upon knowledge of the camera’s focus settings, or the precise location of the camera. It is, however, marred by numerous other requirements which make the method unreliable.