Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.

OBJECTIVE: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. METHODS: In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes. RESULTS: Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing. CONCLUSIONS: Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans

Precision Optical Measurements and Fundamental Physical Constants

A brief overview is given on precision determinations of values of the fundamental physical constants and the search for their variation with time by means of precision spectroscopy in the optical domain

Observer agreement for small bowel ultrasound in Crohn's disease: results from the METRIC trial

PURPOSE: To prospectively evaluate interobserver agreement for small bowel ultrasound (SBUS) in newly diagnosed and relapsing Crohn's disease. METHODS: A subset of patients recruited to a prospective trial comparing the diagnostic accuracy of MR enterography and SBUS underwent a second SBUS performed by one of a pool of six practitioners, who recorded the presence, activity and location of small bowel and colonic disease. Detailed segmental mural and extra-mural observations were also scored. Interobserver variability was expressed as percentage agreement with a construct reference standard, split by patient cohort, grouping disease as present or absent. Prevalence adjusted bias adjusted kappa (PABAK), and simple percentage agreement between practitioners, irrespective of the reference standard, were calculated. RESULTS: Thirty-eight patients (11 new diagnosis, 27 relapse) were recruited from two sites. Overall percentage agreement for small bowel disease presence against the consensus reference was 82% (52-95% (95%CI)), kappa coefficient (κ) 0.64, (substantial agreement) for new diagnosis and 81%, κ 0.63 (substantial agreement) for the relapsing cohort. Agreement for colonic disease presence was 64%, κ 0.27 (fair agreement) in new diagnosis and 78%,κ 0.56 (moderate agreement) in the relapsing cohort. Simple agreement between practitioners was 84% and 87% for small bowel and colonic disease presence respectively. Practitioners agreed on small bowel disease activity in 24/27 (89%) where both identified disease. Kappa agreement for detailed mural observations ranged from κ 0.00 to 1.00. CONCLUSION: There is substantial practitioner agreement for small bowel disease presence in newly diagnosed and relapsing CD patients, supporting wider dissemination of enteric US

Epithelial-mesenchymal plasticity determines estrogen receptor positive breast cancer dormancy and epithelial reconversion drives recurrence

More than 70% of human breast cancers (BCs) are estrogen receptor α-positive (ER+). A clinical challenge of ER+ BC is that they can recur decades after initial treatments. Mechanisms governing latent disease remain elusive due to lack of adequate in vivo models. We compare intraductal xenografts of ER+ and triple-negative (TN) BC cells and demonstrate that disseminated TNBC cells proliferate similarly as TNBC cells at the primary site whereas disseminated ER+ BC cells proliferate slower, they decrease CDH1 and increase ZEB1,2 expressions, and exhibit characteristics of epithelial-mesenchymal plasticity (EMP) and dormancy. Forced E-cadherin expression overcomes ER+ BC dormancy. Cytokine signalings are enriched in more active versus inactive disseminated tumour cells, suggesting microenvironmental triggers for awakening. We conclude that intraductal xenografts model ER + BC dormancy and reveal that EMP is essential for the generation of a dormant cell state and that targeting exit from EMP has therapeutic potential

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Malignant melanoma and bone resorption

The cellular and humoral mechanisms accounting for osteolysis in skeletal metastases of malignant melanoma are uncertain. Osteoclasts, the specialised multinucleated cells that carry out bone resorption, are derived from monocyte/macrophage precursors. We isolated tumour-associated macrophages (TAMs) from metastatic (lymph node/skin) melanomas and cultured them in the presence and absence of osteoclastogenic cytokines and growth factors. The effect of tumour-derived fibroblasts and melanoma cells on osteoclast formation and resorption was also analysed. Melanoma TAMs (CD14+/CD51−) differentiated into osteoclasts (CD14−/CD51+) in the presence of receptor activator for nuclear factor κB ligand (RANKL) and macrophage-colony stimulating factor. Tumour-associated macrophage-osteoclast differentiation also occurred via a RANKL-independent pathway when TAMs were cultured with tumour necrosis factor-α and interleukin (IL)-1α. RT–PCR showed that fibroblasts isolated from metastatic melanomas expressed RANKL messenger RNA and the conditioned medium of cultured melanoma fibroblasts was found to be capable of inducing osteoclast formation in the absence of RANKL; this effect was inhibited by the addition of osteoprotegerin (OPG). We also found that cultured human SK-Mel-29 melanoma cells produce a soluble factor that induces osteoclast differentiation; this effect was not inhibited by OPG. Our findings indicate that TAMs in metastatic melanomas can differentiate into osteoclasts and that melanoma fibroblasts and melanoma tumour cells can induce osteoclast formation by RANKL-dependent and RANKL-independent mechanisms, respectively

Substantial Seasonal Contribution of Observed Biogenic Sulfate Particles to Cloud Condensation Nuclei

Biogenic sources contribute to cloud condensation nuclei (CCN) in the clean marine atmosphere, but few measurements exist to constrain climate model simulations of their importance. The chemical composition of individual atmospheric aerosol particles showed two types of sulfate-containing particles in clean marine air masses in addition to mass-based Estimated Salt particles. Both types of sulfate particles lack combustion tracers and correlate, for some conditions, to atmospheric or seawater dimethyl sulfide (DMS) concentrations, which means their source was largely biogenic. The first type is identified as New Sulfate because their large sulfate mass fraction (63% sulfate) and association with entrainment conditions means they could have formed by nucleation in the free troposphere. The second type is Added Sulfate particles (38% sulfate), because they are preexisting particles onto which additional sulfate condensed. New Sulfate particles accounted for 31% (7 cm−3) and 33% (36 cm−3) CCN at 0.1% supersaturation in late-autumn and late-spring, respectively, whereas sea spray provided 55% (13 cm−3) in late-autumn but only 4% (4 cm−3) in late-spring. Our results show a clear seasonal difference in the marine CCN budget, which illustrates how important phytoplankton-produced DMS emissions are for CCN in the North Atlantic

Organic matter identifies the nano-mechanical properties of native soil aggregates

Nanoscale mechanical properties of soil organic matter has been linked to macroscale soil behaviour via optimising QNM-AFM for rough and heterogeneous surface analysis, specifically intact soil micro-aggregates and particles

Stochastic population growth in spatially heterogeneous environments

Classical ecological theory predicts that environmental stochasticity increases extinction risk by reducing the average per-capita growth rate of populations. To understand the interactive effects of environmental stochasticity, spatial heterogeneity, and dispersal on population growth, we study the following model for population abundances in $n$ patches: the conditional law of $X_{t+dt}$ given $X_t=x$ is such that when $dt$ is small the conditional mean of $X_{t+dt}^i-X_t^i$ is approximately $[x^i\mu_i+\sum_j(x^j D_{ji}-x^i D_{ij})]dt$, where $X_t^i$ and $\mu_i$ are the abundance and per capita growth rate in the $i$-th patch respectivly, and $D_{ij}$ is the dispersal rate from the $i$-th to the $j$-th patch, and the conditional covariance of $X_{t+dt}^i-X_t^i$ and $X_{t+dt}^j-X_t^j$ is approximately $x^i x^j \sigma_{ij}dt$. We show for such a spatially extended population that if $S_t=(X_t^1+...+X_t^n)$ is the total population abundance, then $Y_t=X_t/S_t$, the vector of patch proportions, converges in law to a random vector $Y_\infty$ as $t\to\infty$, and the stochastic growth rate $\lim_{t\to\infty}t^{-1}\log S_t$ equals the space-time average per-capita growth rate \sum_i\mu_i\E[Y_\infty^i] experienced by the population minus half of the space-time average temporal variation \E[\sum_{i,j}\sigma_{ij}Y_\infty^i Y_\infty^j] experienced by the population. We derive analytic results for the law of $Y_\infty$, find which choice of the dispersal mechanism $D$ produces an optimal stochastic growth rate for a freely dispersing population, and investigate the effect on the stochastic growth rate of constraints on dispersal rates. Our results provide fundamental insights into "ideal free" movement in the face of uncertainty, the persistence of coupled sink populations, the evolution of dispersal rates, and the single large or several small (SLOSS) debate in conservation biology.Comment: 47 pages, 4 figure