Unambiguous Inference Research Articles

Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM).MethodsSGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years).ResultsPatients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition.ConclusionsThese results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Spectral graph modeling reveals global slowing of neurophysiological network transmission in Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) is the most common form of dementia, progressively impairing memory and cognition. While various neuroimaging studies have revealed functional network abnormalities in patients with AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. In order to answer these questions, we employed a spectral graph‐theory based model (SGM) to identify abnormal biophysical markers of neuronal activity in AD.MethodSGM is an analytic model that describes how the long‐range fiber projections in the brain mediate and couple the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled models, the SGM is linear, available in closed‐form, and parameterized by a small set of global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well characterized clinical population of patients with AD (N = 88) and a cohort of age‐matched healthy controls (N = 88). We estimated SGM parameters that best captured the regional magnetoencephalography (MEG) frequency spectra of patients with AD and age‐matched controls.ResultSGM was able to capture the regional MEG frequency spectra of patients with AD and healthy controls, specifically, the abnormal slowing of neural oscillations in AD (Fig 1A). Patients with AD have significantly elevated long‐range excitatory neuronal time constant (tG) compared to controls (p < 0.001). Time constant tG is also the most important feature for accurate classification of patients with AD from controls (Area Under Curve = 0.85; Fig 1B). Lastly, higher tG is negatively correlated with global cognitive decline score (p = 0.02; Fig 1C).ConclusionThese results indicate that a global impairment in the long‐range excitatory neuronal activity is associated with abnormal spectral signatures and cognitive deficits in AD. Intriguingly, our work is able to recapitulate the spatial and spectral patterns of AD‐related functional activity without introducing any spatial heterogeneity whatsoever; indeed, the SGM model is entirely global and spatially‐invariant. This raises the possibility that a global increase in the long‐range excitatory time constant might be a sufficient factor underlying observed spatiotemporal alterations of neuronal activity in AD. Our findings provide new insights into potential mechanistic links between abnormal neural oscillations and their cellular correlates in AD.

Abstract 246: Method to analyze mutational and phenotypic profiles from single cell for clonal evolution

Abstract Single cell multiomics assays targeting RNA and Protein from the same cell provide a high-resolution view of the heterogeneity of the sample. However both analytes target the phenotype of the cells and unambiguous inference that a cellular phenotype is caused by a genotype can only be achieved by their measurement from the same single cell. To address this gap, we have developed the Tapestri multi-omics workflow to analyze the DNA and Protein information from the same cell. After pre-processing the reads, cell calling is performed using DNA reads. Variant calling and filtering is carried out using DNA reads to identify the high quality genetic variants within each cell and the variant-cell matrix is then analyzed to identify clones. The Protein reads for the valid cells are log normalized. A systemic artefact resulting in random uniform amplification of antibodies is corrected for by learning the scaling factor from the read count distribution. This reduces the number of false positives. Furthermore read depth dependence of expression is corrected by identifying the correlation of the mean expression and background counts with the total reads in the cell. The mean signal and background for each cell is learned using a 2 component gaussian mixture model. Then, z-score normalization is applied to prevent high expressors from skewing further analysis. The scaled values are dimensionally reduced using PCA. A graph structure is created using KNN with weights calculated using Jaccard similarity following which community detection is employed to identify the cell types. We test this method on a model system with two donor PBMC, 2 cell lines mixture titrated at a 47:47:5:1 ratio with a 312 amplicon DNA panel and 45 plex antibody panel. We filter cells using isotype controls from the antibody panel and mixed cell signatures using the DNA panel. We can identify 4 clones using the genetic variants. We can identify multiple cell types including the major populations such as T-cells, B-cells, Monocytes and NK-cells using the phenotypic expression in the PBMC donors. Moreover, we are able to correlate the proportions of each PBMC cell type for the identified clones with that of the individual donors. Due to the corrections applied during the normalization of protein counts we are also able to accurately identify various expected expression profiles in PBMCs such as CD45RA-CD45RO and CD8-CD4 mutual exclusion, and the trimodal expression of CD4. Citation Format: Saurabh Parikh, Manimozhi Manivannan, Jacqueline Marin, Kate Thompson, Daniel Mendoza, Benjamin Schroeder, Saurabh Gulati, Shu Wang, Aik Ooi. Method to analyze mutational and phenotypic profiles from single cell for clonal evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 246.

Abstract 865: Using machine learning to detect heterogeneity in single cell multi-omics datasets

Abstract Background: While scRNA-seq helps in obtaining high-resolution views of single-cell heterogeneity through characterization of the functional state of cells, our understanding of the cellular properties and population architectures of heterogeneous tissues will be greatly advanced by the multi-omics investigation of single cells. Recently, several computational methods have been developed to integrate data from multiple different single cell experiments measuring individual analytes. But unambiguous inference that a cellular phenotype is caused by a genotype can only be achieved by their measurement from the same single cell. To address this gap, we have developed the Tapestri multi-omics workflow to analyze the RNA and DNA information from the same cell. Methods: After pre-processing of the reads, cell calling is done by identifying the good barcodes using both DNA and RNA reads. DNA reads are processed to identify genetic variants in each cell. The variant cell matrix is then filtered for data completeness to ensure only high-quality data is used in downstream processing. Ploidy is estimated using by normalizing DNA reads, genetic variants and ploidy information together is used to identify subclones. The RNA reads are log normalized and scaled within each cell. Next we set the mean expression of each transcript to 0 and scale the variance to 1. This avoids downstream analysis being skewed by high expressors. We then trained a random forest classifier to identify significantly differentially expressed transcripts across subclones which were identified using genetic variants and ploidy information. Results: Using the top differentially expressed transcripts we performed dimensionality reduction followed by clustering of cell types. The resulting visualization showed how well the genotypic and transcriptomic datasets integrated with one another. We tested this method on a model system with Raji and KG1 cell lines titrated at 50:50 ratio and were clearly able to associate the transcriptional variation with the genotypic variation of the 2 different cell lines. The method was also validated on a PBMC sample to ensure robustness of methods. We were able to identify the different cell types present the sample and were able to overlay that information with genetic variants to identify sub-clones in the identified cell types. Citation Format: Saurabh Gulati, Shu Wang, Saurabh Parikh, Ben Liu, Kaustubh Gokhale, Manimozhi Manivannan, Sombeet Sahu, Dong Kim, Anup Parikh. Using machine learning to detect heterogeneity in single cell multi-omics datasets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 865.

Consciousness as a logically consistent and prognostic model of reality

The work demonstrates that brain might reflect the external world causal relationships in the form of a logically consistent and prognostic model of reality, which shows up as consciousness. The paper analyses and solves the problem of statistical ambiguity and provides a formal model of causal relationships as probabilistic maximally specific rules. We suppose that brain makes all possible inferences from causal relationships. We prove that the suggested formal model has a property of an unambiguous inference: from consistent premises we infer a consistent conclusion. It enables a set of all inferences to form a consistent model of the perceived world. Causal relationships may create fixed points of cyclic inter-predictable properties. We consider the “natural” classification introduced by John St. Mill and demonstrate that a variety of fixed points of the objects’ attributes forms a “natural” classification of the external world. Then we consider notions of “natural” categories and causal models of categories, introduced by Eleanor Rosch and Bob Rehder and demonstrate that fixed points of causal relationships between objects attributes, which we perceive, formalize these notions. If the “natural” classification describes the objects of the external world, and “natural” concepts the perception of these objects, then the theory of integrated information, introduced by G. Tononi, describes the information processes of the brain for “natural” concepts formation that reflects the “natural” classification. We argue that integrated information provides high accuracy of the objects identification. A computer-based experiment is provided that illustrates fixed points formation for coded digits.

Modelled isotopic fractionation and transient diffusive release of methane from potential subsurface sources on Mars

We calculate transport timescales of martian methane and investigate the effect of potential release mechanisms into the atmosphere using a numerical model that includes both Fickian and Knudsen diffusion. The incorporation of Knudsen diffusion, which improves on a Fickian description of transport given the low permeability of the martian regolith, means that transport timescales from sources collocated with a putative martian water table are very long, up to several million martian years. Transport timescales also mean that any temporally varying source process, even in the shallow subsurface, would not result in a significant, observable variation in atmospheric methane concentration since changes resulting from small variations in flux would be rapidly obscured by atmospheric transport. This means that a short-lived 'plume' of methane, as detected by Mumma et al. (2009) and Webster et al. (2014), cannot be reconciled with diffusive transport from any reasonable depth and instead must invoke alternative processes such as fracturing or convective plumes.It is shown that transport through the martian regolith will cause a significant change in the isotopic composition of the gas, meaning that methane release from depth will produce an isotopic signature in the atmosphere that could be significantly different than the source composition. The deeper the source, the greater the change, and the change in methane composition in both δ13C and δD approaches -1000‰ for sources at a depth greater than around 1km. This means that signatures of specific sources, in particular the methane produced by biogenesis that is generally depleted in 13CH4 and CH3D, could be obscured. We find that an abiogenic source of methane could therefore display an isotopic fractionation consistent with that expected for biogenic source processes if the source was at sufficient depth. The only unambiguous inference that can be made from measurements of methane isotopes alone is a measured δ13C or δD close to zero or positive implies a shallow, abiogenic source. The effect of transport processes must therefore be carefully considered when attempting to identify the source of any methane observed by future missions, and the severe depletion in heavier isotopologues will have implications for the sensitivity requirements for future missions that aim to measure the isotopic fractionation of methane in the martian atmosphere.

Rule randomisation and its application to logic-based workflow verification

Workflow verification has been a well-studied research topic during the past few years. Theorem proof-based approaches to workflow verification have become popular due to several advantages including being based on formal characterisation with rigorous and unambiguous inference mechanisms. However, a common problem with these inference mechanisms is combinatorial explosions, which forms a major performance hurdle to workflow verification systems based on inference. In this paper, we study how randomisation enables reuse and reduces processing time in logic-based workflow verification approaches. In particular, we look at a propositional logic-based workflow verification technique. For the logic inference rules, which are used to infer new truthful propositions from existing truthful propositions in this logic, we apply randomisation to the inference rules after each verification task such that new inference rules, reflecting the componentised verification, are added to the inference rule sets. We also review the savings gained by verifying a workflow pattern and provide a theoretical analysis.

TU‐C‐351‐10: Inference of Hysteretic Respiratory Tumour Motion From External Surrogates: A State Augmentation Approach

Purpose: To infer internal respiration‐induced tumor motion from external surrogate. To systematically resolve mapping ambiguity caused by breathing hysteresis. Method and Materials: We propose a state‐augmentation approach to capture system dynamics. Concatenating real‐time surrogate observations with their time‐delayed records describes the state information in a higher‐dimensional state space. In such space, inhale and exhale “stages” are naturally separated due to the incorporated velocity contents. Any existing inference model migrates effortlessly into this framework. We illustrate the idea with simple polynomial inference models, and derive a closed‐form solution for optimal choice of model parameters. Choice of lag length is demonstrated empirically to be robust and may be chosen offline. This approach is tested on synchronized recordings of internal tumor trajectories and external fiducial marker readouts from eight lung patients (multiple fractions and readings) with a Mitsubishi real‐time radiotherapy (RTRT) system. Internal recording is obtained by fluoroscopic tracking of implanted 1.5mm‐diameter gold ball bearings around the tumor and external surrogates measure relative abdominal skin positions. Results: Examination of trajectories in the augmented state‐space suggests the existence of a consistent and unambiguous inference map. Empirical tests with clinical data show that using state augmentation decreases the 3D RMSE from 2.01mm to 1.74mm with the linear model and 1.93mm to 1.63 with the quadratic model. Paired student‐t tests with P‐values on the order of 10e‐13 indicate statistical significance of the improvement. Conclusion: We proposed a simple state‐augmentation approach to implicitly incorporate the hysteretic internal‐external response pattern into the estimation framework with any existing inference model. For the general class of correspondence models that are linear in their parameters, closed‐form solutions for the optimal parameter values and the error evaluations are derived. Tests with clinical data demonstrate statistically significant improvement over direct models.This work is sponsored by NIH P01‐CA59827 and Barbour Scholarship.

Can DAGs Clarify Effect Modification?

The system proposed by VanderWeele and Robins for categorization of effect modifiers that are causal nodes in a directed acyclic graph (DAG) was not intended to empower DAGs to fully represent complex interactions among causes. However, once one has algebraically identified effect modifiers, the DAG implies a role for them. The limitations of epidemiologic definitions of "effect modification" are discussed, along with the implications of scale dependency for assessing interactions, where the scale can be either absolute risk, relative risk, or odds. My view is that probabilistic independence leads to the log-complement as a natural scale for interaction, but even that scale does not necessarily admit unambiguous inference. Any 2 direct causes of D are effect modifiers for each other on at least 2 scales, which can make a reasonable person question the utility of the concept. Still, etiologic models for joint effects are important, because most diseases arise through pathways involving multiple factors. I suggest an enhancement in construction of DAGs in epidemiology that includes arrow-on-arrow representations for effect modification. Examples are given, some of which depend on scale and some of which do not. An example illustrates possible biologic implications for such an effect modification DAG.

Direct observation and unambiguous inference

In science, it sometimes occurs that an event is directly observed, and on other occasions that it is not directly observed but one can make the unambiguous inference that it has occurred. Is there any difference concerning the analysis of data arising from these two situations? In this note we show that there is such a difference in one case arising frequently in genetics. The difference derives from the fact that the ability to make the unambiguous inference arises only from a restricted form of data.

Deterioration in learning and memory of inferential tasks for evaluation of transitivity and symmetry in aged SAMP8 mice.

This study examined age-dependent deficits in the learning and memory of inferential tasks, using an established senescence-accelerated mouse model in age-related brain dysfunction (SAMP8) and its genetically related inbred strain (SAMR1). The mice learned two sets of nonspatial odor-odor pairs by association learning successively (i.e., A-->B, X-->Y, then B-->C, Y-->Z). They were tested in transitive inference (i.e., A-->C, X-->Z) and symmetrical inference (i.e., C-->B, Z-->Y). In the probe test of A-->C, X-->Z transitive inference, 1-month-old SAMP8 and control SAMR1 at the same age significantly chose the alternative based on transitive inference, but 4- and 7-month-old SAMP8 performed at a random chance level, in comparison with unambiguous inference by control SAMR1 at the same ages. During the test of C-->B, Z-->Y symmetrical inference, SAMP8 at 1 month of age made errors as frequently as control SAMR1 at the same age, but SAMP8 at 4 and 7 months of age made more errors than SAMR1 at the same ages. At 4 and 7 months of age, SAMP8 made more errors than 1-month-old SAMP8. Control SAMR1 did not show such an age-related deficient. These results indicate that SAMP8 mice have age-related learning and memory deficits in the ability to perform inferential tasks. Age-related hippocampal dysfunction is suggested to be the cause of these age-related deficits in old SAMP8 mice during the performance of inferential tasks mediated by declarative memory.

Numerical modelling of natural water colour: Implications for remote sensing and limnological studies

Based on suggested earlier optical models of Lakes Ontario and Ladoga, numerical modelling experiments have been carried out to the effect of revealing the responsiveness of chromaticity coordinates (x, y, z), dominant wavelength (lambda dom) and associated spectral purity (p) to the abundance in water of optically active components (OAC), i.e., phytoplankton (chl), suspended minerals (sm) and dissolved organic carbon (doc). It has been shown that highly turbid waters (i.e., waters with high chl and sm concentrations) with low content of doc display colour varying from green to brownish. High turbidity or large doc concentration invariably is characteristic of waters with brown colour. With growing OAC content in water, the chromaticity coordinates and, consequently, the dominant wavelength lambda dom tend asymptotically to respective limit values that seem to be intrinsically characteristic of natural waters. It is also shown that the colour purity p asymptotically tends to values of about 35-45 per cent (with the only exception for waters containing exclusively chl and this in small quantities (0.5mu gl-1)) when concentrations of one or more OAC are over 10 (in respective concentration units). These findings clearly indicate that neither panchromatic nor two-channel ratio images could be meaningful for an unambiguous inference of water quality parameters. Furthermore, the correspondence between water colour physical characteristics (i.e., x, y, z, lambda dom and p) and water colour scales traditionally used in limnology is established.

Modelling the Radiometric Color of Inland Waters: Implications to a) Remote Sensing and b) Limnological Color Scales

Utilizing a bio-optical model previously developed for Lake Ontario, the responsiveness of chromaticity coordinates (X, Y, Z), dominant wavelength (λ dom), and associated spectral purity (p) to the abundance of color-producing agents (CPA) residing within the Lake Ladoga water column was determined. CPA considered were phytoplankton (chl), suspended minerals (sm), and dissolved organic carbon (doc). Waters that contain simultaneously low concentrations of chl, sm, and doc are shown to appear blue to turquoise in color (472–500 nm). Highly turbid waters (i.e., waters containing high concentrations of chl and/or sm) with low concentrations of doc are shown to display colors ranging from green to brown (> 500 nm). Waters with large concentrations of doc, irrespective of turbidity, are shown to be invariably brownish (560–570 nm). With increasing CPA content, X, Y, and Z (and, consequently, λ dom) asymptotically approach constant limit values. An “end-point” dominant wavelength at about 572 nm appears to be intrinsically characteristic of all natural waters. It is shown that when one or more CPA exceeds a critical concentration, the spectral purity p asymptotically approaches values in the range 0.35 to 0.45 for all waters (exceptive of those containing solely chl in the restricted concentration range < 0.5 μg/L). Optical distinctiveness, particularly with respect to indigenous doc, of natural waters, while impacting the spectral purity of the “end-point” radiometric color, does not produce a comparable impact on the “end-point” color itself. This work reinforces the restrictive application of chromaticity analyses to the remote sensing of binary aquatic systems comprised of water plus one CPA. It also illustrates that neither panchromatic nor two-channel ratio images can provide unambiguous inference of water quality parameters. Correspondence between radiometric water color descriptors (X, Y, Z, λ dom, and p) and water color scales traditionally used in limnology is established, illustrating that the platinum-cobalt scale would be most appropriate for assessing waters that were radiometrically yellow, provided that the yellow hue were not invariably attributed to doc.

Interest Rates, the Emergency Fund Hypothesis and Saving through Endowment Policies: Some Empirical Evidence for the U.K.

Interest Rates, the Emergency Fund Hypothesis and Saving through Endowment Policies: Some Empirical Evidence for the U.K. Introduction Historical trends in most industrial countries indicate that, compared to other forms of financial saving, saving through life insurance has been declining over a long period. Explaining these trends requires examining the factors determining household saving through life insurance. In this connection, various hypotheses have been advanced in the literature, but few have been subject to rigorous empirical testing. (1) This article provides econometric tests of some of these hypotheses for the endowment policies written by British life insurers, using time series data for 1952 through 1985. Apart from these tests, the results also provide estimates of the degree to which saving through life insurance is sensitive to its various determinants. The model of saving through life insurance used in this study is first outlined. Then empirical results are discussed and hypotheses about the relevance of various factors (e.g., interest rates) that have been suggested in the literature as exerting an important influence on such saving are tested. Households can adjust their saving through life insurance in a number of ways. Typically though, the main effects operate through changes in the allocation of new wealth to life policies and through surrenders. The empirical results based on the model estimated in this study do not, however, allow unambiguous inference about the effects on policy surrenders, which represent the main mechanism via which households adjust their existing holdings of the life insurance asset. Consequently additional evidence is provided on the hypotheses as they pertain to policy surrenders. These findings are, however, specific to one segment of the British life insurance sector: endowment policies. In conclusion, the findings are summarized and their implications discussed. Saving through Life Insurance: The Model Various options are available to holders of endowment policies written by British life insurers, in the event they wish to respond to changing capital market conditions by modifying their saving through these policies. They can simply forfeit their policies as they would term policies; they can make the policies paid-up, whereby, even though they cease making premium payments, they retain the benefits these policies offer; they can seek policy loans on the surrender value; or, they can seek a cash surrender. Two hypotheses in the literature attempt to explain changes in household saving through life insurance, via the mechanisms mentioned above: (i) the interest rate hypothesis, and (ii) the emergency fund hypothesis. The former argues that saving through life insurance is sensitive to rates of return; positively to internal rates of return on endowment policies, and negatively to rates of return on other financial assets. The hypothesis has been examined with respect to policy loans in the United States by a number of researchers [see for example, Hogan (1970), Schott (1971), Pesando (1974), and Cummins (1978)]. The latter hypothesis, originally proposed by Linton (1932), is based on the premise that households regard their savings in life policies as a source of emergency funds which they can draw upon in times of need (e.g., during recession-induced unemployment), either by taking out policy loans or by seeking surrenders. Cummins (1975) tested this hypothesis for the United States for both policy loans and surrenders. His results did appear to support the hypothesis, particularly for surrenders during the 1950s. In the following section, British data on endowment policies are used to examine these hypotheses in terms of the sensitivity of saving through endowment policies to changes in their own rates of return and alternative rates of return, and in a variable constructed to reflect emergency fund considerations. …