Calibration with confidence:A principled method for panel assessment

Frequently, a set of objects has to be evaluated by a panel of assessors, but not every object is assessed by every assessor. A problem facing such panels is how to take into account different standards amongst panel members and varying levels of confidence in their scores. Here, a mathematically-based algorithm is developed to calibrate the scores of such assessors, addressing both of these issues. The algorithm is based on the connectivity of the graph of assessors and objects evaluated, incorporating declared confidences as weights on its edges. If the graph is sufficiently well connected, relative standards can be inferred by comparing how assessors rate objects they assess in common, weighted by the levels of confidence of each assessment. By removing these biases, "true" values are inferred for all the objects. Reliability estimates for the resulting values are obtained. The algorithm is tested in two case studies, one by computer simulation and another based on realistic evaluation data. The process is compared to the simple averaging procedure in widespread use, and to Fisher's additive incomplete block analysis. It is anticipated that the algorithm will prove useful in a wide variety of situations such as evaluation of the quality of research submitted to national assessment exercises; appraisal of grant proposals submitted to funding panels; ranking of job applicants; and judgement of performances on degree courses wherein candidates can choose from lists of options.Comment: 32 pages including supplementary information; 5 figure

Optimality of mutation and selection in germinal centers

The population dynamics theory of B cells in a typical germinal center could play an important role in revealing how affinity maturation is achieved. However, the existing models encountered some conflicts with experiments. To resolve these conflicts, we present a coarse-grained model to calculate the B cell population development in affinity maturation, which allows a comprehensive analysis of its parameter space to look for optimal values of mutation rate, selection strength, and initial antibody-antigen binding level that maximize the affinity improvement. With these optimized parameters, the model is compatible with the experimental observations such as the ~100-fold affinity improvements, the number of mutations, the hypermutation rate, and the "all or none" phenomenon. Moreover, we study the reasons behind the optimal parameters. The optimal mutation rate, in agreement with the hypermutation rate in vivo, results from a tradeoff between accumulating enough beneficial mutations and avoiding too many deleterious or lethal mutations. The optimal selection strength evolves as a balance between the need for affinity improvement and the requirement to pass the population bottleneck. These findings point to the conclusion that germinal centers have been optimized by evolution to generate strong affinity antibodies effectively and rapidly. In addition, we study the enhancement of affinity improvement due to B cell migration between germinal centers. These results could enhance our understandings to the functions of germinal centers.Comment: 5 figures in main text, and 4 figures in Supplementary Informatio

Positive Semidefiniteness and Positive Definiteness of a Linear Parametric Interval Matrix

We consider a symmetric matrix, the entries of which depend linearly on some parameters. The domains of the parameters are compact real intervals. We investigate the problem of checking whether for each (or some) setting of the parameters, the matrix is positive definite (or positive semidefinite). We state a characterization in the form of equivalent conditions, and also propose some computationally cheap sufficient\,/\,necessary conditions. Our results extend the classical results on positive (semi-)definiteness of interval matrices. They may be useful for checking convexity or non-convexity in global optimization methods based on branch and bound framework and using interval techniques

Predictive feedback control and Fitts' law

Fitts’ law is a well established empirical formula, known for encapsulating the “speed-accuracy trade-off”. For discrete, manual movements from a starting location to a target, Fitts’ law relates movement duration to the distance moved and target size. The widespread empirical success of the formula is suggestive of underlying principles of human movement control. There have been previous attempts to relate Fitts’ law to engineering-type control hypotheses and it has been shown that the law is exactly consistent with the closed-loop step-response of a time-delayed, first-order system. Assuming only the operation of closed-loop feedback, either continuous or intermittent, this paper asks whether such feedback should be predictive or not predictive to be consistent with Fitts law. Since Fitts’ law is equivalent to a time delay separated from a first-order system, known control theory implies that the controller must be predictive. A predictive controller moves the time-delay outside the feedback loop such that the closed-loop response can be separated into a time delay and rational function whereas a non- predictive controller retains a state delay within feedback loop which is not consistent with Fitts’ law. Using sufficient parameters, a high-order non-predictive controller could approximately reproduce Fitts’ law. However, such high-order, “non-parametric” controllers are essentially empirical in nature, without physical meaning, and therefore are conceptually inferior to the predictive controller. It is a new insight that using closed-loop feedback, prediction is required to physically explain Fitts’ law. The implication is that prediction is an inherent part of the “speed-accuracy trade-off”

Inverse spin-s portrait and representation of qudit states by single probability vectors

Using the tomographic probability representation of qudit states and the inverse spin-portrait method, we suggest a bijective map of the qudit density operator onto a single probability distribution. Within the framework of the approach proposed, any quantum spin-j state is associated with the (2j+1)(4j+1)-dimensional probability vector whose components are labeled by spin projections and points on the sphere. Such a vector has a clear physical meaning and can be relatively easily measured. Quantum states form a convex subset of the 2j(4j+3) simplex, with the boundary being illustrated for qubits (j=1/2) and qutrits (j=1). A relation to the (2j+1)^2- and (2j+1)(2j+2)-dimensional probability vectors is established in terms of spin-s portraits. We also address an auxiliary problem of the optimum reconstruction of qudit states, where the optimality implies a minimum relative error of the density matrix due to the errors in measured probabilities.Comment: 23 pages, 4 figures, PDF LaTeX, submitted to the Journal of Russian Laser Researc

Intraspecfic variation in cold-temperature metabolic phenotypes of Arabidopsis lyrata ssp petraea

Atmospheric temperature is a key factor in determining the distribution of a plant species. Alongside this, plant populations growing at the margin of their range may exhibit traits that indicate genetic differentiation and adaptation to their local abiotic environment. We investigated whether geographically separated marginal populations of Arabidopsis lyrata ssp. petraea have distinct metabolic phenotypes associated with exposure to cold temperatures. Seeds of A. petraea were obtained from populations along a latitudinal gradient, namely Wales, Sweden and Iceland and grown in a controlled cabinet environment. Mannose, glucose, fructose, sucrose and raffinose concentrations were different between cold treatments and populations, especially in the Welsh population, but polyhydric alcohol concentrations were not. The free amino acid compositions were population specific, with fold differences in most amino acids, especially in the Icelandic populations, with gross changes in amino acids, particularly those associated with glutamine metabolism. Metabolic fingerprints and profiles were obtained. Principal component analysis (PCA) of metabolite fingerprints revealed metabolic characteristic phenotypes for each population and temperature. It is suggested that amino acids and carbohydrates were responsible for discriminating populations within the PCA. Metabolite fingerprinting and profiling has proved to be sufficiently sensitive to identify metabolic differences between plant populations at different atmospheric temperatures. These findings show that there is significant natural variation in cold metabolism among populations of A. l. petraea which may signify plant adaptation to local climates

Whole genome sequencing of single circulating tumor cells from neuroendocrine neoplasms

Single-cell profiling of circulating tumor cells (CTCs) as part of a minimally invasive liquid biopsy presents an opportunity to characterize and monitor tumor heterogeneity and evolution in individual patients. In this study, we aimed to compare single-cell copy number variation (CNV) data with tissue, and define the degree of intra- and inter-patient genomic heterogeneity. We performed next generation sequencing (NGS) whole genome CNV analysis of 125 single CTCs derived from seven patients with neuroendocrine neoplasms (NEN) alongside matched white blood cells (WBC), formalin fixed paraffin embedded (FFPE) and fresh frozen (FF) samples. CTC CNV profiling demonstrated recurrent chromosomal alterations in previously reported NEN copy number hotspots, including the prognostically relevant loss of chromosome 18. Unsupervised hierarchical clustering revealed CTCs with distinct clonal lineages as well as significant intra- and inter-patient genomic heterogeneity, including subclonal alterations not detectable by bulk analysis and previously unreported in NEN. Notably, we also demonstrate the presence of genomically distinct CTCs according to the enrichment strategy utilized (EpCAM-dependent versus size-based). This work has significant implications for the identification of therapeutic targets, tracking of evolutionary change and the implementation of CTC-biomarkers in cancer

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Self-ordered pointing and visual conditional associative learning tasks in drug-free schizophrenia spectrum disorder patients

<p>Abstract</p> <p>Background</p> <p>There is evidence of a link between schizophrenia and a deficit of working memory, but this has been derived from tasks not specifically developed to probe working memory per se. Our aim was to investigate whether working memory deficits may be detected across different paradigms using the self-ordered pointing task (SOPT) and the visual conditional associative learning task (VCALT) in patients with schizophrenia spectrum disorders and healthy controls. The current literature suggests deficits in schizophrenia spectrum disorder patients versus healthy controls but these studies frequently involved small samples, broad diagnostic criteria, inclusion of patients on antipsychotic medications, and were not controlled for symptom domains, severity of the disorder, etc. To overcome some of these limitations, we investigated the self-monitoring and conditional associative learning abilities of a numerically representative sample of healthy controls and a group of non-deteriorated, drug-free patients hospitalized for a schizophrenia spectrum disorder with florid, mainly positive psychotic symptoms.</p> <p>Methods</p> <p>Eighty-five patients with a schizophrenia spectrum disorder (DSM-IV-TR diagnosis of schizophrenia (<it>n </it>= 71) or schizophreniform disorder (<it>n </it>= 14)) and 80 healthy controls entered the study. The clinical picture was dominated by positive symptoms. The healthy control group had a negative personal and family history of schizophrenia or mood disorder and satisfied all the inclusion and exclusion criteria other than variables related to schizophrenia spectrum disorders.</p> <p>Results</p> <p>Compared to controls, patients had worse performances on SOPT, VCALT and higher SOPT/VCALT ratios, not affected by demographic or clinical variables. ROC curves showed that SOPT, VCALT, and SOPT/VCALT ratio had good accuracy in discriminating patients from controls. The SOPT and VCALT scores were inter-correlated in controls but not in patients.</p> <p>Conclusion</p> <p>The selection of a clinically homogeneous group of patients, controlled for a number of potential confounding factors, and the high level of significance found in the different analyses confirm the presence of SOPT and VCALT abnormalities in a large preponderance of patients with schizophrenia spectrum disorder with positive symptoms. SOPT, VCALT, and SOPT/VCALT ratio showed good accuracy in discriminating patients from healthy controls. These conclusions cannot be extended to schizophrenia spectrum disorder patients with a different clinical profile from our patient population.</p