Application of Moina mongolica and Daphniopsis tibetana in marine ecotoxicology studies in China: A review.

Moina mongolica and Daphniopsis tibetana are typical saline Cladocera in China that are characterized by a wide salinity range, rapid reproduction, and high-density culture. In this paper, the characteristics and life history of M. mongolica and D. tibetana are reviewed. The application of these two species of Cladocera to ecotoxicology in recent years is also summarized from the aspects of environmental factors and environmental pollutants, including ultraviolet B radiation, temperature, salinity, alkalinity, pH, heavy metals, harmful red tide, pesticides, and persistent organic pollutants. Additionally, the toxicity sensitivity of saline Cladocera in different reproductive statuses and inter-generational embryos is discussed. Finally, the need to enhance knowledge of the molecular genomics, population dynamics, and strategies for protection of saline Cladocera, along with the need for establishment of estuarine and marine environmental monitoring standards are discussed. Overall, this review highlights the potential for using these Cladocera species as indicator organisms for estuarine and marine ecotoxicology studies.

A machine learning approach to determine the prognosis of patients with Class III malocclusion.

INTRODUCTION: The conundrum of determining how to treat a patient with Class III malocclusion is significant, creating a burden on the patient and challenging the orthodontist. The objective of this study was to employ a statistical prediction model derived from our previous cephalometric data on 5 predominant subtypes of skeletal Class III malocclusion to test the hypothesis that Class III subtypes are associated with treatment modalities (eg, surgical vs nonsurgical) and treatment outcome. METHODS: Pretreatment lateral cephalometric records of 148 patients were digitized for 67 cephalometric variables, and measurements were applied to a mathematical equation to assign a Class III subtype. Subjects were assigned to either a surgical or nonsurgical group depending on the treatment received. Treatment outcome was determined by facial profile and clinical photographs. Log binomial models were used for statistical analysis. RESULTS: Subtype 1 (mandibular prognathic) patients were 3.5 × more likely to undergo orthognathic surgery than subtypes 2/3 (maxillary deficient) and 5.3 × more likely than 4/5 (combination). Subtype 1 patients were also 1.5 × more likely to experience treatment failure than subtypes 2/3 (maxillary deficient) and 4/5 (combination). CONCLUSIONS: This assessment of a systematic method to characterize patients with Class III malocclusion into subtypes revealed that subtype 1 (mandibular prognathic) showed a likelihood to undergo orthognathic surgery while subtypes 2/3 experienced significantly lower treatment failure (in response to orthodontics alone). Further refinement of the equation may yield a reliable prediction model for earlier identification of surgical patients and also provide predictive power of Class III treatment outcomes.

Blunted circadian cortisol in children is associated with poor cardiovascular health and may reflect circadian misalignment.

OBJECTIVES: Circadian cues in children (sunlight, exercise, diet patterns) may be associated with health outcomes. The primary objective was to assess associations of daily cortisol fluctuations (morning, night) with cardiovascular health outcomes. A secondary objective was to determine if 1-year longitudinal changes in circadian cortisol levels are associated with longitudinal changes in health outcomes. STUDY DESIGN: The Cardiovascular Health Intervention Program (CHIP) was a cross-sectional and longitudinal study of cardiovascular risk profiles in public elementary school children in Southern Maine. Participants were 689 students in 4th grade (baseline; age = 9.20 ± 0.41 years), and 647 students in 5th grade (age = 10.53 ± 0.52 years). Longitudinal data (4th and 5th grade) was available for 347 participants. Clinical outcomes were blood pressure, hip/waist ratios, body mass index, percent fat. Laboratory measures were fasting glucose, lipids, and salivary cortisol measures (morning and evening). RESULTS: Lower first-in-morning diurnal cortisol levels were associated with increased blood pressure (ß -0.23 ± 0.05; p < 0.001), increased body fat (ß -0.22 ± 0.05; p < 0.001), and poor lipid profiles (ß -0.15 ± 0.07; p < 0.05). Inclusion of night cortisol in the model (stress-related) improved associations of the model with bodyfat composition (morning ß -0.27 ± 0.05; p < 0.001; night ß +0.16 ± 0.06; p < 0.01). Adjustments for potential confounding variables improved associations of morning cortisol with lipids (ß -0.19 ± 0.07; p < 0.01). Longitudinal analysis showed that lower morning diurnal cortisol in 4th grade was associated with increases in blood pressure a year later (ß -0.18 ± 0.08; p = 0.017) after adjusting for confounding variables. CONCLUSION: Data presented suggest adding circadian misalignment (lower amplitude of first-in-morning cortisol) to existing models of metabolic syndrome in children. Further, circadian misalignment may be a factor contributing to high blood pressure.

Near-optimal Individualized Treatment Recommendations.

The individualized treatment recommendation (ITR) is an important analytic framework for precision medicine. The goal of ITR is to assign the best treatments to patients based on their individual characteristics. From the machine learning perspective, the solution to the ITR problem can be formulated as a weighted classification problem to maximize the mean benefit from the recommended treatments given patients' characteristics. Several ITR methods have been proposed in both the binary setting and the multicategory setting. In practice, one may prefer a more flexible recommendation that includes multiple treatment options. This motivates us to develop methods to obtain a set of near-optimal individualized treatment recommendations alternative to each other, called alternative individualized treatment recommendations (A-ITR). We propose two methods to estimate the optimal A-ITR within the outcome weighted learning (OWL) framework. Simulation studies and a real data analysis for Type 2 diabetic patients with injectable antidiabetic treatments are conducted to show the usefulness of the proposed A-ITR framework. We also show the consistency of these methods and obtain an upper bound for the risk between the theoretically optimal recommendation and the estimated one. An R package aitr has been developed, found at https://github.com/menghaomiao/aitr.

Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodologies to identify a T2DM-related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity. PURPOSE: To develop a machine-learning pipeline to investigate the method's discriminative value between T2DM patients and normal controls, the T2DM-related network pattern, and association of the pattern with cognitive performance/disease severity. STUDY TYPE: A cross-sectional study and prospective longitudinal study with a 2-year time interval. POPULATION: Seventy-three subjects (41 T2DM patients and 32 controls) aged 50-85 years old at baseline, and 42 subjects (19 T2DM and 23 controls) aged 53-88 years old at 2-year follow-up. FIELD STRENGTH/SEQUENCE: 3T pseudocontinuous arterial spin-labeling MRI. ASSESSMENT: Machine-learning-based pipeline (principal component analysis, feature selection, and logistic regression classifier) to generate the T2DM-related network pattern and the individual scores associated with the pattern. STATISTICAL TESTS: Linear regression analysis with gray matter volume and education years as covariates. RESULTS: The machine-learning-based method is superior to the widely used univariate group comparison method with increased test accuracy, test area under the curve, test positive predictive value, adjusted McFadden's R square of 4%, 12%, 7%, and 24%, respectively. The pattern-related individual scores are associated with diabetes severity variables, mobility, and cognitive performance at baseline (P < 0.05, |r| > 0.3). More important, the longitudinal change of individual pattern scores is associated with the longitudinal change of HbA1c (P = 0.0053, r = 0.64), and baseline cholesterol (P = 0.037, r = 0.51). DATA CONCLUSION: The individual perfusion diabetes pattern score is a highly promising perfusion imaging biomarker for tracing the disease progression of individual T2DM patients. Further validation is needed from a larger study. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:834-844.

The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment.

We carried out metagenomic shotgun sequencing and a metagenome-wide association study (MGWAS) of fecal, dental and salivary samples from a cohort of individuals with rheumatoid arthritis (RA) and healthy controls. Concordance was observed between the gut and oral microbiomes, suggesting overlap in the abundance and function of species at different body sites. Dysbiosis was detected in the gut and oral microbiomes of RA patients, but it was partially resolved after RA treatment. Alterations in the gut, dental or saliva microbiome distinguished individuals with RA from healthy controls, were correlated with clinical measures and could be used to stratify individuals on the basis of their response to therapy. In particular, Haemophilus spp. were depleted in individuals with RA at all three sites and negatively correlated with levels of serum autoantibodies, whereas Lactobacillus salivarius was over-represented in individuals with RA at all three sites and was present in increased amounts in cases of very active RA. Functionally, the redox environment, transport and metabolism of iron, sulfur, zinc and arginine were altered in the microbiota of individuals with RA. Molecular mimicry of human antigens related to RA was also detectable. Our results establish specific alterations in the gut and oral microbiomes in individuals with RA and suggest potential ways of using microbiome composition for prognosis and diagnosis.

A statistical approach to set classification by feature selection with applications to classification of histopathology images.

Set classification problems arise when classification tasks are based on sets of observations as opposed to individual observations. In set classification, a classification rule is trained with N sets of observations, where each set is labeled with class information, and the prediction of a class label is performed also with a set of observations. Data sets for set classification appear, for example, in diagnostics of disease based on multiple cell nucleus images from a single tissue. Relevant statistical models for set classification are introduced, which motivate a set classification framework based on context-free feature extraction. By understanding a set of observations as an empirical distribution, we employ a data-driven method to choose those features which contain information on location and major variation. In particular, the method of principal component analysis is used to extract the features of major variation. Multidimensional scaling is used to represent features as vector-valued points on which conventional classifiers can be applied. The proposed set classification approaches achieve better classification results than competing methods in a number of simulated data examples. The benefits of our method are demonstrated in an analysis of histopathology images of cell nuclei related to liver cancer.

Kernelized partial least squares for feature reduction and classification of gene microarray data.

BACKGROUND: The primary objectives of this paper are: 1.) to apply Statistical Learning Theory (SLT), specifically Partial Least Squares (PLS) and Kernelized PLS (K-PLS), to the universal "feature-rich/case-poor" (also known as "large p small n", or "high-dimension, low-sample size") microarray problem by eliminating those features (or probes) that do not contribute to the "best" chromosome bio-markers for lung cancer, and 2.) quantitatively measure and verify (by an independent means) the efficacy of this PLS process. A secondary objective is to integrate these significant improvements in diagnostic and prognostic biomedical applications into the clinical research arena. That is, to devise a framework for converting SLT results into direct, useful clinical information for patient care or pharmaceutical research. We, therefore, propose and preliminarily evaluate, a process whereby PLS, K-PLS, and Support Vector Machines (SVM) may be integrated with the accepted and well understood traditional biostatistical "gold standard", Cox Proportional Hazard model and Kaplan-Meier survival analysis methods. Specifically, this new combination will be illustrated with both PLS and Kaplan-Meier followed by PLS and Cox Hazard Ratios (CHR) and can be easily extended for both the K-PLS and SVM paradigms. Finally, these previously described processes are contained in the Fine Feature Selection (FFS) component of our overall feature reduction/evaluation process, which consists of the following components: 1.) coarse feature reduction, 2.) fine feature selection and 3.) classification (as described in this paper) and prediction. RESULTS: Our results for PLS and K-PLS showed that these techniques, as part of our overall feature reduction process, performed well on noisy microarray data. The best performance was a good 0.794 Area Under a Receiver Operating Characteristic (ROC) Curve (AUC) for classification of recurrence prior to or after 36 months and a strong 0.869 AUC for classification of recurrence prior to or after 60 months. Kaplan-Meier curves for the classification groups were clearly separated, with p-values below 4.5e-12 for both 36 and 60 months. CHRs were also good, with ratios of 2.846341 (36 months) and 3.996732 (60 months). CONCLUSIONS: SLT techniques such as PLS and K-PLS can effectively address difficult problems with analyzing biomedical data such as microarrays. The combinations with established biostatistical techniques demonstrated in this paper allow these methods to move from academic research and into clinical practice.

Weighted Distance Weighted Discrimination and Its Asymptotic Properties.

While Distance Weighted Discrimination (DWD) is an appealing approach to classification in high dimensions, it was designed for balanced datasets. In the case of unequal costs, biased sampling, or unbalanced data, there are major improvements available, using appropriately weighted versions of DWD (wDWD). A major contribution of this paper is the development of optimal weighting schemes for various nonstandard classification problems. In addition, we discuss several alternative criteria and propose an adaptive weighting scheme (awDWD) and demonstrate its advantages over nonadaptive weighting schemes under some situations. The second major contribution is a theoretical study of weighted DWD. Both high-dimensional low sample-size asymptotics and Fisher consistency of DWD are studied. The performance of weighted DWD is evaluated using simulated examples and two real data examples. The theoretical results are also confirmed by simulations.

Adaptive weighted learning for unbalanced multicategory classification.

In multicategory classification, standard techniques typically treat all classes equally. This treatment can be problematic when the dataset is unbalanced in the sense that certain classes have very small class proportions compared to others. The minority classes may be ignored or discounted during the classification process due to their small proportions. This can be a serious problem if those minority classes are important. In this article, we study the problem of unbalanced classification and propose new criteria to measure classification accuracy. Moreover, we propose three different weighted learning procedures, two one-step weighted procedures, as well as one adaptive weighted procedure. We demonstrate the advantages of the new procedures, using multicategory support vector machines, through simulated and real datasets. Our results indicate that the proposed methodology can handle unbalanced classification problems effectively.