Traditional Statistical Approaches Research Articles

Deep Learning-Based Firework Video Pattern Classification

The video analytics technology has been a rapidly improving discipline in the past decade. With the recent developments in computer vision, we now have the ability to mine massive video data to obtain a clear understanding of what is happening in the world. Because of the remarkable successes of deep learning, currently, we are able to improve video analysis performance significantly than traditional statistical approaches. This study focuses on classifying the patterns of firework videos with various deep learning techniques using spatial and temporal features, beyond common types of pattern classifications. Among successful artificial neural networks, Convolutional Neural Networks (CNN) have demonstrated superiority on modeling high-level visual concepts, while Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) units have shown great talent in modeling temporal dynamics in video-based pattern classification. Our basic models consist of CNN, LSTM, and GRU and, we did experiments by fine-tuning the parameters of layers and using different dropout values with sequence LSTM and GRU models. Our experimental results demonstrated that the model with a sequence of LSTM units and double dropout layers—one for input and another for hidden layers—outperforms the other experimental models with the training accuracy of 83.05%.

A Novel Key Influencing Factors Selection Approach of P2P Lending Investment Risk

Recent frequent “thunderstorm incidents” of P2P lending industry have caused the panic of industry investors. To predict the investment risk of P2P lending, we should scientifically and rationally analyze the key influencing factors of P2P lending investment risk. Existing key influencing factors selection methods mainly involve traditional statistical approaches and artificial intelligence methods. The traditional statistical approaches cannot deal with the high-dimensional nonlinear problems, and it cannot find the exact key influencing factors of the P2P lending investment risk. The artificial intelligence methods cannot recognize and learn the application background, and the selected attributes without active thinking and personal perception may not be the key influencing factors of P2P lending investment risk. To address the above issues, a novel key influencing factors selection approach of P2P lending investment risk is proposed by combining the proposed fireworks coevolution binary glowworm swarm optimization (FCBGSO), multifractal dimension (MFD), probit regression, and artificial prior knowledge. First, multifractal dimension combined with the proposed FCBGSO is used to select the preliminary influencing factors of the investment risk; second, the nonsignificant relevant attributes in the preliminary influencing factors are removed using the probit regression, and we add the influencing factors extracted from the original dataset of P2P lending using the artificial prior knowledge into the retaining influencing factors after removing one by one. A small and reasonable number of influencing factor subsets are achieved. Finally, we evaluate each influencing factors subset using extreme learning machine (ELM), and the subset with the best classification accuracy is efficiently achieved, i.e., it is the key influencing factors of P2P lending investment risk. Experimental results on the real P2P lending dataset from the Renrendai platform demonstrate that the proposed approach performs better than other state-of-the-art methods and that it has validity and effectiveness. It provides a new research idea for the key influencing factors selection of P2P lending investment risk.

Unfolding the Effects of Acute Cardiovascular Exercise on Neural Correlates of Motor Learning Using Convolutional Neural Networks.

Cardiovascular exercise is known to promote the consolidation of newly acquired motor skills. Previous studies seeking to understand the neural correlates underlying motor memory consolidation that is modulated by exercise, have relied so far on using traditional statistical approaches for a priori selected features from neuroimaging data, including EEG. With recent advances in machine learning, data-driven techniques such as deep learning have shown great potential for EEG data decoding for brain-computer interfaces, but have not been explored in the context of exercise. Here, we present a novel Convolutional Neural Network (CNN)-based pipeline for analysis of EEG data to study the brain areas and spectral EEG measures modulated by exercise. To the best of our knowledge, this work is the first one to demonstrate the ability of CNNs to be trained in a limited sample size setting. Our approach revealed discriminative spectral features within a refined frequency band (27–29 Hz) as compared to the wider beta bandwidth (15–30 Hz), which is commonly used in data analyses, as well as corresponding brain regions that were modulated by exercise. These results indicate the presence of finer EEG spectral features that could have been overlooked using conventional hypothesis-driven statistical approaches. Our study thus demonstrates the feasibility of using deep network architectures for neuroimaging analysis, even in small-scale studies, to identify robust brain biomarkers and investigate neuroscience-based questions.

Changes in soil organic carbon and its influencing factors in the growth of Pinus sylvestris var. mongolica plantation in Horqin Sandy Land, Northeast China

The change of soil organic carbon and its influencing factors after afforestation in sandy land should be taken into account. Here, the factors would be revealed which would influence the SOC dynamics to a depth of 100 cm during the development of Mongolian pine plantations in Horqin sandy land, northeast China. The chronosequence method was used to quantify the change of SOC in vertical distribution and influencing factors following conversion grassland to Pinus sylvestris var. mongolica forest in semi-arid sandy land, northeast China. Then the traditional statistical approaches were used to assessed the influence of the identified factors. Stand age played a major role in SOC dynamics. It took 38 years for SOC in 0–10 cm layer to recover to its initial level after afforestation, and 46 years for 10–20 cm layer. SOC accumulation increased with the age of Mongolian pine plantation. Over-mature forest fully embodied the advantage of SOC accumulation. In addition, the changes of SOC in 0–10 cm layer were also affected by TN, TP, TK and soil moisture, and those below 10 cm soil layers were related to the effects of TN, TP, TK, BD and CS.

The Influence of Animosity, Ethnocentric Tendencies, and National Attachment on Tourists’ Decision-Making Processes during International Conflicts

This study expands the empirical evidence amassed to date concerning tourists’ decision-making processes in a situation of conflict between a country of origin’s market and country of destination. It uses the constructs of animosity, national attachment, consumer ethnocentrism, country image, and perceptions of the state of bilateral relations in order to examine three intent to visit scenarios. To obtain the robust results, the study uses two methods of analysis from distinctly different repertoires of techniques, such as traditional statistical and data-mining approaches, and applies them to each scenario. The results consistently indicate a high level of influence of general animosity and ethnocentric tendencies in addition to country image and bilateral relation variables in tourists’ decision making. The situational context of the study is the conflict between China, the country of origin, and South Korea, the country of destination, over the Terminal High Altitude Area Defense (THAAD) system.

Energy load forecasting model based on deep neural networks for smart grids

In recent, smart grid has emerged as a promising technology to facilitate the future electric power grid and to balance between supply and demand. However, the intermittent nature of distributed energy resources causes dynamic uncertainties and nonlinearity in the smart grid environment. This may result in a large stress on power grid and has a big influence on energy planning, especially the generation and distribution. Therefore, energy load forecasting plays an important role in facilitating the operation of the future smart grid. Using the traditional statistical and machine learning approach there exists a significant forecasting error and high degree of overfitting. In this paper, we propose an energy load forecasting (ELF) model based on deep neural network architectures to manage the energy consumption in smart grids. First we investigate the applicability of two deep neural network architectures: deep feed-forward neural network (deep-FNN) and deep recurrent neural network (deep-RNN). To evaluate the models with low error, we simulate both architectures with multi size training set. Further, various activation functions and different combinations of hidden layer architectures are also tested. The simulation results are compared in terms of mean absolute percentage error. The results show that the proposed ELF model has attained better generalization and outperform the existing load forecasting models based on the shallow neural network, ensemble tree bagger and generalized linear regression.

Data-driven prediction models of multi-dimensional energy consumed in public buildings

Due to the intense pressure from energy shortage and environmental protection, an accurate prediction of building energy consumption is crucial for different energy conservation applications and policies. Besides simulation models and traditional statistical approaches, a data-driven modelling based on energy records provides new opportunities for predicting the building energy demand. This research is conducted based on the whole procedure of data mining with limited datasets, by making use of machine learning techniques and mathematical statistics. Especially, regarding the temporal and the architectural scales, models can be categorized into the short-term prediction, medium-term prediction and long-term prediction of classified energy consumptions, which also represent different modelling characteristics derived from mass data, limited data and poor data respectively. During the modelling process, the fuzzy C-means clustering and the interdisciplinary Lorenz curve were utilized to recognize different energy patterns. Afterwards, models of the nonlinear Support Vector Regression, the Grey model and the traditional polynomial regression were utilized respectively to output the predicted sequence. In summary, with datasets in current energy platforms, this paper presents a study of data-driven models based on energy records considering the nonlinear and uncertain features of different multi-dimensional models.

An unsupervised learning approach to study synchroneity of past events in the South China Sea

Unsupervised machine learning methods were applied on multivariate geophysical and geochemical datasets of ocean floor sediment cores collected from the South China Sea. The well-preserved and continuous core samples comprising high resolution Cenozoic sediment records enable scientists to carry out paleoenvironment studies in detail. Bayesian age-depth chronological models constructed from biostratigraphic control points for the drilling sites are applied on cluster boundaries generated from two popular unsupervised learning methods: K-means and random forest. The unsupervised learning methods experimented have produced compact and unambiguous clusters from the datasets, indicating that previously unknown data patterns can be revealed when all variables from the datasets are taken into account simultaneously. A study of synchroneity of past events represented by the cluster boundaries across geographically separated ocean drilling sites is achieved through converting the fixed depths of cluster boundaries into chronological ranges represented by Gaussian density plots which are then compared with known past events in the region. A Gaussian density peak at around 7.2 Ma has been identified from results of all three sites and it is suggested to coincide with the initiation of the East Asian monsoon. Contrary to traditional statistical approach, a priori assumptions are not required for unsupervised learning, and the clustering results serve as a novel data-driven proxy for studying the complex and dynamic processes of the paleoenvironment surrounding the ocean sediment. This work serves as a pioneering approach to extract valuable information of regional events and opens up a systematic and objective way to study the vast global ocean sediment datasets.

Osteobiography: A Platform for Bioarchaeological Research.

Osteobiography provides a rich basis for understanding the past, but its conceptual framework has not been outlined systematically. It stands in conceptual opposition to a traditional statistical approach to bioarchaeology modeled upon clinical studies in biomedicine, but is interdependent with it. As such, its position mirrors those of clinical case histories as opposed to statistical studies, participant-observation ethnography as opposed to quantitative sociology, and microhistory and biography as opposed to quantitative history. Such disciplinary comparisons provide a framework for exploring the strengths and weaknesses of osteobiography. It is not merely a tool for engagingly illustrating the "typical" life history as established statistically. Rather, it allows us to understand issues that population studies cannot explore. These include both analytical directions (exploring the complexity of deeply layered data, understanding the role of contingency in human lives, integrating osteological and cultural evidence) and philosophical directions (the interaction of material and conceptual factors in the creation of human bodies, embodiment, the experience of time).

Machine learning in M4: What makes a good unstructured model?

The Makridakis Competitions seek to identify the most accurate forecasting methods for different types of predictions. The M4 competition was the first in which a model of the type commonly described as “machine learning” has outperformed the more traditional statistical approaches, winning the competition. However, many approaches that were self-labeled as “machine learning” failed to produce accurate results, which generated discussion about the respective benefits and drawbacks of “statistical” and “machine learning” approaches. Both terms have remained ill-defined in the context of forecasting. This paper introduces the terms “structured” and “unstructured” models to better define what is intended by the use of the terms “statistical” and “machine learning” in the context of forecasting based on the model’s data generating process. The mechanisms that underlie specific challenges to unstructured modeling are examined in the context of forecasting, along with common solutions. Finally, the innovations in the winning model that allowed it to overcome these challenges and produce highly accurate results are highlighted.

Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data. This paper develops a novel convolutional approach that addresses the drawbacks of both traditional statistical approaches as well as recent neural network models for survival. We present Match-Net: a missingness-aware temporal convolutional hitting-time network, designed to capture temporal dependencies and heterogeneous interactions in covariate trajectories and patterns of missingness. To the best of our knowledge, this is the first investigation of temporal convolutions in the context of dynamic prediction for personalized risk prognosis. Using real-world data from the Alzheimer's disease neuroimaging initiative, we demonstrate state-of-the-art performance without making any assumptions regarding underlying longitudinal or time-to-event processes-attesting to the model's potential utility in clinical decision support.

Where do taxi trips start and end? Implications For Future Car-sharing For Commuting

Abstract. The impact of built environment on travel behaviour has long been an actively researched topic. However, many researches are focusing on the travel behaviour regarding to the choice of passengers on using public transport such as buses and subways, or private means such as private cars. Taxi, as a historical mean of public transport has not been widely researched in term of its relationship with the built environment.Considering taxi as a practical, comfortable, and direct (door-to-door) means of transport, it is still widely used alongside with other available public transport modes either as a supplemental mode toward the buses and subways, or to accomplish the whole journey of a passenger. Of particular interest to transportation planners and policymakers is that a taxi trip which drives a passenger from a home to a bus stop or subway station, and vice versa might signifies its uses as either a first-mile trip or last-mile trip provider, supporting the inability of our public transit system to solve that problem in general. Therefore, the first research question of this paper is to investigate whether taxi has been able to increase urban accessibility via providing connection between homes and transit stations (i.e. subway stations or bus stops). Our model will control for a series of built environment features, especially the number of jobs within 100m of a transit station. Note that some commuters’ taxi drop-off locations are indeed their workplace while others are simply the access points where they enter the public transit network (a.k.a. the first mile issue we mentioned). We use the variable ‘number of jobs within 100m of a transit station’ to rule out the blurring effect of the first scenario in which drop-off locations are workplaces. This is an important identification strategy for our first research question.Aside from its function as a first-mile trip or last-mile trip provider and its relationship with the urban built environment, taxi has been commonly accepted as a mean of transport with significantly higher fare than either bus or subway, which is certainly an indicator of passengers’ economic profile. In the meantime, racial minority groups tend to live in relatively separated areas, as referred in the residential segregation and spatial mismatch literature. These racial minority neighbourhoods, or racial and ethnic enclaves, often do not have good accessibility to public transit network. If we do find taxi is serving as a first-mile or last-mile trip provider, how is it related to the residential segregation of racial minority groups? Is it simply an affordability issue (i.e. determined by income)? Or are there something more that are inherent in the phenomenon of residential segregation? With this in mind, the second research question of this paper is to investigate whether taxi originations have been disproportionately low in racial minority neighbourhoods, after taking into account of income and a variety of other demographic and socioeconomic factors. If it is so, (relatively) affordable car sharing schemes provided by Uber, Lyft, and other subsidized vanpool programs might become very important for connecting these minority neighbourhoods to existing transit networks. The findings of this part would have important implications for equitable transportation planning and policymaking.Combining big data analytics and traditional spatial statistical approach, the contributions of this paper are mainly two folds. First, it helps us understand whether taxi has been able to increase urban accessibility via providing connection between homes and transit stations (i.e. subway stations or bus stops). Second, it provides insights on whether taxi originations have been disproportionately low in racial minority neighbourhoods, raising transport equity concerns over a simple affordability issue.This research aims to examine 1) whether taxi has been able to increase urban accessibility via providing connection between homes and transit stations (i.e. subway stations or bus stops); 2) whether taxi originations have been disproportionately low in racial minority neighbourhoods, raising transport equity concerns over a simple affordability issue.

Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis

Abstract Condition monitoring signals obtained from rotating machines often demonstrate a highly non-stationary and transient nature due to internal natural deterioration characteristics of their constituent components and external time-varying operational conditions. Traditional multivariate statistical monitoring approaches are based on the assumption that the underlying processes are linear and static and are apt to interpret the normal changes in operating conditions as faults, which would result in high false positive rates. On the other hand, the development of robust diagnostic techniques for the detection of incipient faults remains a challenge for researchers, given the difficulty of finding an appropriate trade-off between a low false positive ratio and early detection of emerging faults. To address these issues, this paper proposes a novel adaptive fault detection approach based on the canonical residuals (CR) induced by the combination of canonical variate analysis (CVA) and matrix perturbation theory for the monitoring of dynamic processes where variations in operating conditions are incurred. The canonical residuals are calculated based upon the distinctions between past and future measurements and are able to effectively detect emerging faults while still maintaining a low false positive rate. The effectiveness of the developed diagnostic model for the detection of abnormalities in industrial processes was demonstrated for slow involving faults in case studies of two operational industrial high-pressure pumps. In comparison with the variable-based and canonical correlation-based statistical monitoring approaches, the proposed canonical residuals-adaptive canonical variate analysis (CR-ACVA) fault detection method has demonstrated its superiorities by the detailed performance comparisons.

Innovations: Brave new worlds.

The field of cytopathology, from fine-needle aspiration to computer-assisted screening, has always attracted innovators and creative thinkers. Technology touches every aspect of our world and our cytopathology practice. Opportunities and challenges both are in abundant supply. In this new “Innovations” section, we will highlight the latest developments in technology, social media, and informatics to keep readers up to date. Social media has added new layers of interaction and networking capabilities to our field. The pathology and cytopathology communities have harnessed the power of these technologies to build stronger professional networks, share and discuss the latest research findings, and even conduct multi-institutional research studies. Social media platforms make the world smaller, enabling education and collaborations across continents. Journal clubs now can involve participants from across the country. Twitter chats regarding topics such as the Milan System, ancillary testing in cytology, and noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) flatten hierarchies and allow unprecedented real-time access to experts in the field (Fig. 1). Our recent #CytoChat on the topic of NIFTP featured a real-time discussion of practice patterns, the sharing of useful articles, and expert input from Drs. Faquin and Rossi. This chat drew greater than 60 participants from around the world, garnering greater than 1 million potential impressions. Not sure how to get started with social media? A previously published article provides an overview of what social media can do for you, and how to get started.1 To make sure you do not miss out, follow Cancer Cytopathology on Facebook (https://www.facebook.com/CancerCytopath) and Twitter (https://twitter.com/CancerCytopath). The complexity of workflows and the need to meet quality assurance mandates have precipitated the computerization of the cytology laboratory to improve effectiveness and efficiency. As an early adopter of pathology informatics and its data management principle, the cytology laboratory has in some ways defined how data are collected, accessed, and analyzed. Another area of informatics to which cytology has made early contributions is digital pathology. Ironically, although one of the first successful implementations of digital pathology was the automation of the Papanicolaou test, progress in this realm of informatics has been delayed due to the unique challenges present in cytologic specimens and the use of whole-slide imaging (WSI). Nevertheless, computational progress and improved imaging technology have allowed pathology informatics to enter a period of rapid growth and innovation. Using improved WSI technology and z-stacking modalities to overcome the challenges of digitizing 3-dimensional cytologic material, leading investigators in the field of cytopathology have begun to exploit the advantages offered by digital pathology over traditional microscopy. To that end, this new and exciting section of Cancer Cytopathology will focus on highlighting innovative applications of pathology informatics in the cytology laboratory, including the use of traditional statistical tools and advanced deep-learning approaches for data mining and visualization. In an effort to increase visibility, we also will showcase how digital pathology continues to revolutionize the clinical practice of cytology through the application of WSI in telecytology for rapid on-site evaluation, remote consultation, and digital image analysis for diagnostic testing. To kick off this issue of Cancer Cytopathology, we would like to congratulate Dr. Louis J. Vaickus from Dartmouth-Hitchcock Medical Center in Lebanon, New Hampshire, for winning this year's Young Investigator Challenge (Fig. 2). His work explored the development of a hybrid deep-learning and morphometric algorithm for the analysis of urine cytology specimens.2 This breakthrough article presents the framework for a system that can automate the Paris System for Urine Cytopathology reliably, with the potential to increase the efficiency of digital screening for urine WSI. Xiaoyin “Sara” Jiang, MD, is an associate professor of pathology at Duke University in Durham, North Carolina, and the associate director of the Duke BioRepository and Precision Pathology Center. Her areas of research and expertise are cytopathology and surgical pathology of the head and neck and endocrine systems, with particular interest in thyroid nodules, ultrasound-guided fine-needle aspiration, and novel applications of social media for medical professionals. Emilio Madrigal, DO, is a Cuban-born anatomic and clinical pathologist specializing in cytopathology and clinical informatics at the Massachusetts General Hospital in Boston, Massachusetts. He has special interests in salivary gland cytology and machine intelligence–powered digital image analysis. Dr. Madrigal received his Doctor of Osteopathic Medicine from Rowan University in Glassboro, New Jersey and has been certified by the American Board of Pathology in anatomic and clinical pathology and cytopathology.

Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology

Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia.

Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium.

BackgroundMachine learning (ML) allows the exploration and progressive improvement of very complex high-dimensional data patterns that can be utilised to optimise specific classification and prediction tasks, outperforming traditional statistical approaches. An enormous acceleration of ready-to-use tools and artificial intelligence (AI) applications, shaped by the emergence, refinement, and application of powerful ML algorithms in several areas of knowledge, is ongoing. Although such progress has begun to permeate the medical sciences and clinical medicine, implementation in cardiovascular medicine and research is still in its infancy.ObjectivesTo lay out the theoretical framework, purpose, and structure of a novel AI consortium.MethodsWe have established a new Dutch research consortium, the CVON-AI, supported by the Netherlands Heart Foundation, to catalyse and facilitate the development and utilisation of AI solutions for existing and emerging cardiovascular research initiatives and to raise AI awareness in the cardiovascular research community. CVON-AI will connect to previously established CVON consortia and apply a cloud-based AI platform to supplement their planned traditional data-analysis approach.ResultsA pilot experiment on the CVON-AI cloud was conducted using cardiac magnetic resonance data. It demonstrated the feasibility of the platform and documented excellent correlation between AI-generated ventricular function estimates as compared to expert manual annotations. The resulting AI solution was then integrated in a web application.ConclusionCVON-AI is a new consortium meant to facilitate the implementation and raise awareness of AI in cardiovascular research in the Netherlands. CVON-AI will create an accessible cloud-based platform for cardiovascular researchers, demonstrate the clinical applicability of AI, optimise the analytical methodology of other ongoing CVON consortia, and promote AI awareness through education and training.

Differential Effects of Influenza Virus NA, HA Head, and HA Stalk Antibodies on Peripheral Blood Leukocyte Gene Expression during Human Infection.

In this study, we examined the relationships between anti-influenza virus serum antibody titers, clinical disease, and peripheral blood leukocyte (PBL) global gene expression during presymptomatic, acute, and convalescent illness in 83 participants infected with 2009 pandemic H1N1 virus in a human influenza challenge model. Using traditional statistical and logistic regression modeling approaches, profiles of differentially expressed genes that correlated with active viral shedding, predicted length of viral shedding, and predicted illness severity were identified. These analyses further demonstrated that challenge participants fell into three peripheral blood leukocyte gene expression phenotypes that significantly correlated with different clinical outcomes and prechallenge serum titers of antibodies specific for the viral neuraminidase, hemagglutinin head, and hemagglutinin stalk. Higher prechallenge serum antibody titers were inversely correlated with leukocyte responsiveness in participants with active disease and could mask expression of peripheral blood markers of clinical disease in some participants, including viral shedding and symptom severity. Consequently, preexisting anti-influenza antibodies may modulate PBL gene expression, and this must be taken into consideration in the development and interpretation of peripheral blood diagnostic and prognostic assays of influenza infection.IMPORTANCE Influenza A viruses are significant human pathogens that caused 83,000 deaths in the United States during 2017 to 2018, and there is need to understand the molecular correlates of illness and to identify prognostic markers of viral infection, symptom severity, and disease course. Preexisting antibodies against viral neuraminidase (NA) and hemagglutinin (HA) proteins play a critical role in lessening disease severity. We performed global gene expression profiling of peripheral blood leukocytes collected during acute and convalescent phases from a large cohort of people infected with A/H1N1pdm virus. Using statistical and machine-learning approaches, populations of genes were identified early in infection that correlated with active viral shedding, predicted length of shedding, or disease severity. Finally, these gene expression responses were differentially affected by increased levels of preexisting influenza antibodies, which could mask detection of these markers of contagiousness and disease severity in people with active clinical disease.

Approaches to Computational Strain Design in the Multiomics Era.

Modern omics analyses are able to effectively characterize the genetic, regulatory, and metabolic phenotypes of engineered microbes, yet designing genetic interventions to achieve a desired phenotype remains challenging. With recent developments in genetic engineering techniques, timelines associated with building and testing strain designs have been greatly reduced, allowing for the first time an efficient closed loop iteration between experiment and analysis. However, the scale and complexity associated with multi-omics datasets complicates manual biological reasoning about the mechanisms driving phenotypic changes. Computational techniques therefore form a critical part of the Design-Build-Test-Learn (DBTL) cycle in metabolic engineering. Traditional statistical approaches can reduce the dimensionality of these datasets and identify common motifs among high-performing strains. While successful in many studies, these methods do not take full advantage of known connections between genes, proteins, and metabolic networks. There is therefore a growing interest in model-aided design, in which modeling frameworks from systems biology are used to integrate experimental data and generate effective and non-intuitive design predictions. In this mini-review, we discuss recent progress and challenges in this field. In particular, we compare methods augmenting flux balance analysis with additional constraints from fluxomic, genomic, and metabolomic datasets and methods employing kinetic representations of individual metabolic reactions, and machine learning. We conclude with a discussion of potential future directions for improving strain design predictions in the omics era and remaining experimental and computational hurdles.

The Heterogeneity of Word Learning Biases in Late-Talking Children.

Purpose The particular statistical approach researchers choose is intimately connected to the way they conceptualize their questions, which, in turn, can influence the conclusions they draw. One particularly salient area in which statistics influence our conclusions is in the context of atypical development. Traditional statistical approaches such as t tests or analysis of variance lend themselves to a focus on group differences, downplaying the heterogeneity that exists within so many atypically developing populations. Understanding such variability is important-classification of what a disorder is, an individual's diagnosis, and whether or not a child receives intervention all directly relate to an accurate classification of the disorder and individual's abilities compared to their typically developing peers. Method Here, we use word learning biases (i.e., shape and material biases) in late-talking children as a sample case and employ a variety of statistical approaches to compare the conclusions those approaches might warrant. Results We argue that advanced statistical approaches, such as mixed-effects regression, can help us make sense of heterogeneity and are more consistent with a modern dimensional view of language disorders. Conclusions Accurate characterization of late-talking children (and others at risk for delays) and their prognoses is necessary for accurate diagnosis and implementation of appropriate target interventions. It therefore requires rigorous statistical analyses that can capture and allow for interpretation of the heterogeneity inherent in populations with language delays and disorders.

Threats to the Validity of Logistics and Supply Chain Management Research

To increase the relevance of logistics and supply chain academic research, this paper recommends the development and testing of middle‐range theory and practice‐level theory. Yet, there are a number of research issues that arise when academic researchers test middle‐range and practice‐level theory, both in measuring constructs and in testing theoretical relationships between constructs. Concerning the measurement of constructs, this paper recommends that academic researchers pay more attention to content validity and undertake rigorous processes to ensure content validity. In addition, academic researchers need to more explicitly define constructs as either reflective or formative. If the construct is defined as formative, then the traditional statistical approaches to validate these measurement scales are not recommended. The appropriate use of employing single‐item measures for concrete constructs is discussed. In regard to conducting hypothesis tests, research issues associated with multicollinearity and omitted variable bias are discussed. Relative weight analysis is ideal for testing theoretical models and research hypotheses when survey data are obtained, multicollinearity is present, and there are a large number of independent variables predicting a dependent variable. Thus, relative weight analysis is ideal for testing research hypotheses in logistics and supply chain management.