Plasma based markers of [11C] PiB-PET brain amyloid burden.

PublishedJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tChanges in brain amyloid burden have been shown to relate to Alzheimer's disease pathology, and are believed to precede the development of cognitive decline. There is thus a need for inexpensive and non-invasive screening methods that are able to accurately estimate brain amyloid burden as a marker of Alzheimer's disease. One potential method would involve using demographic information and measurements on plasma samples to establish biomarkers of brain amyloid burden; in this study data from the Alzheimer's Disease Neuroimaging Initiative was used to explore this possibility. Sixteen of the analytes on the Rules Based Medicine Human Discovery Multi-Analyte Profile 1.0 panel were found to associate with [(11)C]-PiB PET measurements. Some of these markers of brain amyloid burden were also found to associate with other AD related phenotypes. Thirteen of these markers of brain amyloid burden--c-peptide, fibrinogen, alpha-1-antitrypsin, pancreatic polypeptide, complement C3, vitronectin, cortisol, AXL receptor kinase, interleukin-3, interleukin-13, matrix metalloproteinase-9 total, apolipoprotein E and immunoglobulin E--were used along with co-variates in multiple linear regression, and were shown by cross-validation to explain >30% of the variance of brain amyloid burden. When a threshold was used to classify subjects as PiB positive, the regression model was found to predict actual PiB positive individuals with a sensitivity of 0.918 and a specificity of 0.545. The number of APOE [Symbol: see text] 4 alleles and plasma apolipoprotein E level were found to contribute most to this model, and the relationship between these variables and brain amyloid burden was explored.Alzheimer's Disease Neuroimaging Initiative (ADNI)Canadian Institutes of Health ResearchFoundation for the National Institutes of HealthNational Institutes of HealthInnoMed, European Union of the Sixth Framework programNational Institutes for Health Research Biomedical Research Centre for Mental Health at the South London and Maudsley National Health Service Foundation TrustInstitute of Psychiatry, King's College Londo

MF-PCBA: Multifidelity High-Throughput Screening Benchmarks for Drug Discovery and Machine Learning

High-throughput screening (HTS), as one of the key techniques in drug discovery, is frequently used to identify promising drug candidates in a largely automated and cost-effective way. One of the necessary conditions for successful HTS campaigns is a large and diverse compound library, enabling hundreds of thousands of activity measurements per project. Such collections of data hold great promise for computational and experimental drug discovery efforts, especially when leveraged in combination with modern deep learning techniques, and can potentially lead to improved drug activity predictions and cheaper and more effective experimental design. However, existing collections of machine-learning-ready public datasets do not exploit the multiple data modalities present in real-world HTS projects. Thus, the largest fraction of experimental measurements, corresponding to hundreds of thousands of “noisy” activity values from primary screening, are effectively ignored in the majority of machine learning models of HTS data. To address these limitations, we introduce Multifidelity PubChem BioAssay (MF-PCBA), a curated collection of 60 datasets that includes two data modalities for each dataset, corresponding to primary and confirmatory screening, an aspect that we call multifidelity. Multifidelity data accurately reflect real-world HTS conventions and present a new, challenging task for machine learning: the integration of low- and high-fidelity measurements through molecular representation learning, taking into account the orders-of-magnitude difference in size between the primary and confirmatory screens. Here we detail the steps taken to assemble MF-PCBA in terms of data acquisition from PubChem and the filtering steps required to curate the raw data. We also provide an evaluation of a recent deep-learning-based method for multifidelity integration across the introduced datasets, demonstrating the benefit of leveraging all HTS modalities, and a discussion in terms of the roughness of the molecular activity landscape. In total, MF-PCBA contains over 16.6 million unique molecule-protein interactions. The datasets can be easily assembled by using the source code available at https://github.com/davidbuterez/mf-pcba

A Characteristic Mode Analysis of Conductive Nanowires and Microwires Above a Lossy Dielectric Half-Space

Title from PDF of title page viewed October 30, 2017Thesis advisor: Ahmed M. HassanVitaIncludes bibliographical references (pages 55-60)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017Nanowires possess extraordinary mechanical, thermoelectric and electromagnetic properties which led to their incorporation in a wide variety of applications. The purpose of this study is to investigate the effect of material on the electromagnetic response of these nanowires. We used the Method of Moments (MOM) for Arbitrarily Thin Wire (ATW) formulation as an efficient computational technique for calculating the electromagnetic response of nanowires. To explain the calculated electromagnetic response, we evoked the Characteristic Mode Analysis (CMA) which decomposes the current on the wire into a superposition of fundamental current modes. These modes are weighted by two coefficients: (i) the relative importance of each mode at a certain frequency, termed Modal Significance, and (ii) the level of coupling between the incident field and the mode termed the Modal Excitation Coefficient. In this, work we study how the wire’s material affect the Modal Significance and the Modal Excitation Coefficient of nanowires. Our results show that the material of the nanowire has a strong effect on the resonance frequency, the bandwidth, and the overlap of the modes showing that the material of the nanowire can be used as a tuning factor to develop sensors with desired radiation characteristics. Nanowires are commonly grown vertically on a substrate and, therefore, we also study the effect of the presence of a lossy dielectric half-space on their electromagnetic response. To efficiently account for this interface, we utilize a modified Green’s function using the rigorous Sommerfeld integrals. Our results show that the relative permittivity of the substrate decreases the resonance frequencies of the nanowires and significantly alters their radiation patterns. Most importantly, we find that, if the nanowire is near the interface, its evanescent field’s couple to the dielectric half space leading to the majority of the scattered power radiated into the substrate with high directivity. The results of this thesis has the potential to quantify the electromagnetic response of vertical nanowires in their realistic environment as well as facilitate the incorporation of nanowires in novel sensing applications.Introduction -- Theoretical model -- Dielectric and magnetic properties of nanowires and microwires -- Graphical user interface -- Results and discussion -- Conclusion and future wor

Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting

Modern molecular discovery processes generate millions of measurements at different quality levels. Here, the authors develop a new deep learning method for transfer learning from low-cost and abundant data to enhance the efficiency of drug discovery

Graph Neural Networks with Adaptive Readouts

An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. Typically, readouts are simple and non-adaptive functions designed such that the resulting hypothesis space is permutation invariant. Prior work on deep sets indicates that such readouts might require complex node embeddings that can be difficult to learn via standard neighborhood aggregation schemes. Motivated by this, we investigate the potential of adaptive readouts given by neural networks that do not necessarily give rise to permutation invariant hypothesis spaces. We argue that in some problems such as binding affinity prediction where molecules are typically presented in a canonical form it might be possible to relax the constraints on permutation invariance of the hypothesis space and learn a more effective model of the affinity by employing an adaptive readout function. Our empirical results demonstrate the effectiveness of neural readouts on more than 40 datasets spanning different domains and graph characteristics. Moreover, we observe a consistent improvement over standard readouts (i.e., sum, max, and mean) relative to the number of neighborhood aggregation iterations and different convolutional operators

Modelling local and general quantum mechanical properties with attention-based pooling

Atom-centred neural networks represent the state-of-the-art for approximating the quantum chemical properties of molecules, such as internal energies. While the design of machine learning architectures that respect chemical principles has continued to advance, the final atom pooling operation that is necessary to convert from atomic to molecular representations in most models remains relatively undeveloped. The most common choices, sum and average pooling, compute molecular representations that are naturally a good fit for many physical properties, while satisfying properties such as permutation invariance which are desirable from a geometric deep learning perspective. However, there are growing concerns that such simplistic functions might have limited representational power, while also being suboptimal for physical properties that are highly localised or intensive. Based on recent advances in graph representation learning, we investigate the use of a learnable pooling function that leverages an attention mechanism to model interactions between atom representations. The proposed pooling operation is a drop-in replacement requiring no changes to any of the other architectural components. Using SchNet and DimeNet++ as starting models, we demonstrate consistent uplifts in performance compared to sum and mean pooling and a recent physics-aware pooling operation designed specifically for orbital energies, on several datasets, properties, and levels of theory, with up to 85% improvements depending on the specific task

Sensitive Detection of Plasmodium vivax Using a High-Throughput, Colourimetric Loop Mediated Isothermal Amplification (HtLAMP) Platform: A Potential Novel Tool for Malaria Elimination.

INTRODUCTION: Plasmodium vivax malaria has a wide geographic distribution and poses challenges to malaria elimination that are likely to be greater than those of P. falciparum. Diagnostic tools for P. vivax infection in non-reference laboratory settings are limited to microscopy and rapid diagnostic tests but these are unreliable at low parasitemia. The development and validation of a high-throughput and sensitive assay for P. vivax is a priority. METHODS: A high-throughput LAMP assay targeting a P. vivax mitochondrial gene and deploying colorimetric detection in a 96-well plate format was developed and evaluated in the laboratory. Diagnostic accuracy was compared against microscopy, antigen detection tests and PCR and validated in samples from malaria patients and community controls in a district hospital setting in Sabah, Malaysia. RESULTS: The high throughput LAMP-P. vivax assay (HtLAMP-Pv) performed with an estimated limit of detection of 1.4 parasites/ μL. Assay primers demonstrated cross-reactivity with P. knowlesi but not with other Plasmodium spp. Field testing of HtLAMP-Pv was conducted using 149 samples from symptomatic malaria patients (64 P. vivax, 17 P. falciparum, 56 P. knowlesi, 7 P. malariae, 1 mixed P. knowlesi/P. vivax, with 4 excluded). When compared against multiplex PCR, HtLAMP-Pv demonstrated a sensitivity for P. vivax of 95% (95% CI 87-99%); 61/64), and specificity of 100% (95% CI 86-100%); 25/25) when P. knowlesi samples were excluded. HtLAMP-Pv testing of 112 samples from asymptomatic community controls, 7 of which had submicroscopic P. vivax infections by PCR, showed a sensitivity of 71% (95% CI 29-96%; 5/7) and specificity of 93% (95% CI87-97%; 98/105). CONCLUSION: This novel HtLAMP-P. vivax assay has the potential to be a useful field applicable molecular diagnostic test for P. vivax infection in elimination settings

A simple, high-throughput, colourimetric, field applicable loop-mediated isothermal amplification (HtLAMP) assay for malaria elimination.

BACKGROUND: To detect all malaria infections in elimination settings sensitive, high throughput and field deployable diagnostic tools are required. Loop-mediated isothermal amplification (LAMP) represents a possible field-applicable molecular diagnostic tool. However, current LAMP platforms are limited by their capacity for high throughput. METHODS: A high-throughput LAMP (HtLAMP) platform amplifying mitochondrial targets using a 96-well microtitre plate platform, processing 85 samples and 11 controls, using hydroxynaphtholblue as a colourimetric indicator was optimized for the detection of malaria parasites. Objective confirmation of visually detectable colour change results was made using a spectrophotometer. A dilution series of laboratory-cultured 3D7 Plasmodium falciparum parasites was used to determine the limit of detection of the HtLAMP assay, using P. falciparum (HtLAMP-Pf) and Plasmodium genus (HtLAMP-Pg) primers, on whole blood and filter paper, and using different DNA extraction protocols. The diagnostic accuracy of HtLAMP was validated using clinical samples from Papua New Guinea, Malaysia, Ghana and The Gambia and its field applicability was evaluated in Kota Marudu district hospital, Sabah, Malaysia. RESULTS: The HtLAMP assay proved to be a simple method generating a visually-detectable blue and purple colour change that could be objectively confirmed in a spectrophotometer at a wavelength of 600 nm. When compared with PCR, overall HtLAMP-Pg had a sensitivity of 98 % (n = 260/266, 95 % CI 95-99) and specificity 83 % (n = 15/18, 95 % CI 59-96). HtLAMP-Pf had a sensitivity of 97 % (n = 124/128, 95 % CI 92-99) and specificity of 96 % (n = 151/157, 95 % CI 92-99). A validation study in a regional hospital laboratory demonstrated ease of performance and interpretation of the HtLAMP assay. HtLAMP-Pf performed in this field setting had a sensitivity of 100 % (n = 17/17, 95 % CI 80-100) and specificity of 95 % (n = 123/128, 95 % CI 90-98) compared with multiplex PCR. HtLAMP-Pf also performed well on filter paper samples from asymptomatic Ghanaian children with a sensitivity of 88 % (n = 23/25, 95 % CI 69-97). CONCLUSION: This colourimetric HtLAMP assay holds much promise as a field applicable molecular diagnostic tool for the purpose of malaria elimination

The Implementation of a Fast-folding Pipeline for Long-period Pulsar Searching in the PALFA Survey

The Pulsar Arecibo L-Band Feed Array (PALFA) survey, the most sensitive blind search for radio pulsars yet conducted, is ongoing at the Arecibo Observatory in Puerto Rico. The vast majority of the 180 pulsars discovered by PALFA have spin periods shorter than 2 s. Pulsar surveys may miss long-period radio pulsars owing to the summing of a finite number of harmonic components in conventional Fourier analyses (typically ~16), or as a result of the strong effect of red noise at low modulation frequencies. We address this reduction in sensitivity by using a time-domain search technique: the fast-folding algorithm (FFA). We designed a program that implements an FFA-based search in the PALFA processing pipeline and tested the efficiency of the algorithm by performing tests under both ideal, white-noise conditions, as well as with real PALFA observational data. In the two scenarios, we show that the time-domain algorithm has the ability to outperform the FFT-based periodicity search implemented in the survey. We perform simulations to compare the previously reported PALFA sensitivity with that obtained using our new FFA implementation. These simulations show that for a pulsar having a pulse duty cycle of roughly 3%, the performance of our FFA pipeline exceeds that of our FFT pipeline for pulses with dispersion measure lesssim 40 pc cm−3 and for periods as short as ~500 ms, and that the survey sensitivity is improved by at least a factor of two for periods gsim 6 s. Early results from the implementation of the algorithm in PALFA, including discoveries, are also presented in this paper

Blood metabolite markers of neocortical amyloid-β burden:discovery and enrichment using candidate proteins

We believe this is the first study to investigate associations between blood metabolites and neocortical amyloid burden (NAB) in the search for a blood-based biomarker for Alzheimer's disease (AD). Further, we present the first multi-modal analysis of blood markers in this field. We used blood plasma samples from 91 subjects enrolled in the University of California, San Francisco Alzheimer's Disease Research Centre. Non-targeted metabolomic analysis was used to look for associations with NAB using both single and multiple metabolic feature models. Five metabolic features identified subjects with high NAB, with 72% accuracy. We were able to putatively identify four metabolites from this panel and improve the model further by adding fibrinogen gamma chain protein measures (accuracy=79%). One of the five metabolic features was studied in the Alzheimer's Disease Neuroimaging Initiative cohort, but results were inconclusive. If replicated in larger, independent studies, these metabolic features and proteins could form the basis of a blood test with potential for enrichment of amyloid pathology in anti-amyloid trials.</p