Full Quantification Research Articles

Evaluation of Water‐Energy‐Food (WEF) Nexus Research: Perspectives, Challenges, and Directions for Future Research

AbstractIn 2018, Albrecht et al., published a comprehensive review of water‐energy‐food nexus literature, coming to five main criticisms in nexus research based on their perception of the state‐of‐the‐art. The five central conclusions of that review together with a consideration of more recent initiatives form the basis for this critical review. The current state of nexus research, and in particular modeling research, is examined and updated to reflect recent advances, correct some misperceptions, and put the nexus enterprise into the context of larger epistemological issues. The main conclusions of this review are: The considerable and growing diversity in nexus studies necessarily precludes a one‐size‐fits‐all approach, and indeed it has never been an objective to develop a “grand unified nexus theory or model.” A lack of “fundamental equations” between many nexus parameters hinders full quantification of all nexus linkages, though data‐driven, stochastic, and agent‐based approaches offer useful avenues for further development. The use of qualitative and social science methods in nexus studies is rapidly gaining traction, especially when blended with quantitative modeling. Great progress has been made in attempting to break disciplinary siloes, especially when considering integrated assessment models and system dynamics models. The paper concludes with some reflections on how progress to date could inform new initiatives, such as the Sustainable Urban Nexus Project, and future work more generally. These reflections are summarized as follows: Integration of ecosystem services and their relationships to the nexus should be a priority in near‐future nexus research efforts. Social science techniques blended with quantitative modeling efforts, and local stakeholder engagement, are critical to the success and impact of nexus studies and their recommendations. Policy‐relevant recommendations are starting to be developed. Greater focus, and stakeholder involvement, will further the policy impact of nexus studies.

Identification and drug-induced reversion of molecular signatures of Alzheimer\u2019s disease onset and progression in AppNL-G-F, AppNL-F, and 3xTg-AD mouse models

BackgroundIn spite of many years of research, our understanding of the molecular bases of Alzheimer’s disease (AD) is still incomplete, and the medical treatments available mainly target the disease symptoms and are hardly effective. Indeed, the modulation of a single target (e.g., β-secretase) has proven to be insufficient to significantly alter the physiopathology of the disease, and we should therefore move from gene-centric to systemic therapeutic strategies, where AD-related changes are modulated globally.MethodsHere we present the complete characterization of three murine models of AD at different stages of the disease (i.e., onset, progression and advanced). We combined the cognitive assessment of these mice with histological analyses and full transcriptional and protein quantification profiling of the hippocampus. Additionally, we derived specific Aβ-related molecular AD signatures and looked for drugs able to globally revert them.ResultsWe found that AD models show accelerated aging and that factors specifically associated with Aβ pathology are involved. We discovered a few proteins whose abundance increases with AD progression, while the corresponding transcript levels remain stable, and showed that at least two of them (i.e., lfit3 and Syt11) co-localize with Aβ plaques in the brain. Finally, we found two NSAIDs (dexketoprofen and etodolac) and two anti-hypertensives (penbutolol and bendroflumethiazide) that overturn the cognitive impairment in AD mice while reducing Aβ plaques in the hippocampus and partially restoring the physiological levels of AD signature genes to wild-type levels.ConclusionsThe characterization of three AD mouse models at different disease stages provides an unprecedented view of AD pathology and how this differs from physiological aging. Moreover, our computational strategy to chemically revert AD signatures has shown that NSAID and anti-hypertensive drugs may still have an opportunity as anti-AD agents, challenging previous reports.

Drift Estimation of Multiscale Diffusions Based on Filtered Data

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coefficient of the homogenized equation requires pre-processing of the data, often in the form of subsampling; this is because the two-scale equation and the homogenized single-scale equation are incompatible at small scales, generating mutually singular measures on the path space. We avoid subsampling and work instead with filtered data, found by application of an appropriate kernel function, and compute maximum likelihood estimators based on the filtered process. We show that the estimators we propose are asymptotically unbiased and demonstrate numerically the advantages of our method with respect to subsampling. Finally, we show how our filtered data methodology can be combined with Bayesian techniques and provide a full uncertainty quantification of the inference procedure.

Characterization, Stability, and Bioaccessibility of Betalain and Phenolic Compounds from Opuntia stricta var. Dillenii Fruits and Products of Their Industrialization.

The aim of the present study was the full characterization, quantification, and determination of the digestive stability and bioaccessibility of individual betalain and phenolic compounds of Opuntia stricta, var. Dillenii fresh fruits (peel, pulp, and whole fruit) and of the products of the industrialization to obtain jam (raw pressed juice (product used for jam formulation), by-product (bagasse), and frozen whole fruit (starting material for jam production)). Opuntia stricta var. Dillenii fruits and products profile showed 60 betalain and phenolic compounds that were identified and quantified by HPLC-DAD-ESI/MS and HPLC-DAD-MS/QTOF, being 25 phenolic acids (including isomers and derivatives), 12 flavonoids (including glycosides), 3 ellagic acids (including glycosides and derivative), and 20 betanins (including degradation compounds). In vitro gastrointestinal digestion was performed by INFOGEST® protocol. Fruit pulp showed the greater content of total betalains (444.77 mg/100 g f.w.), and jam only showed very low amounts of two betanin degradation compounds, Cyclo-dopa-5-O-β-glucoside (and its isomer) (0.63 mg/100 f.w.), and two Phyllocactin derivatives (1.04 mg/100 g f.w.). Meanwhile, fruit peel was the richer tissue in total phenolic acids (273.42 mg/100 g f.w.), mainly in piscidic acid content and total flavonoids (7.39 mg/100 g f.w.), isorhamnetin glucoxyl-rhamnosyl-pentoside (IG2) being the most abundant of these compounds. The stability of betalains and phenolic compounds during in vitro gastrointestinal digestion is reported in the present study. In Opuntia stricta var. Dillenii pulp (the edible fraction of the fresh fruit), the betanin bioaccessibility was only 22.9%, and the flavonoid bioaccessibility ranged from 53.7% to 30.6%, depending on the compound. In non-edible samples, such as peel sample (PE), the betanin bioaccessibility was 42.5% and the greater bioaccessibility in flavonoids was observed for quercetin glycoside (QG1) 53.7%, the fruit peel being the most interesting material to obtain antioxidant extracts, attending to its composition on antioxidant compounds and their bioaccessibilities.

MO149PREGNANCY AFTER LIVING KIDNEY DONATION, A SYSTEMATIC REVIEW OF THE AVAILABLE EVIDENCE AND A REVIEW OF THE CURRENT GUIDANCE

Abstract Background and Aims It was long believed that living kidney donation did not infer a risk to the health or longevity of living kidney donors (LKD), though recently, some studies have called this into question. The majority of LKD are women of childbearing age, it is therefore vital that a clear picture of the risks associated with pregnancy post-kidney donation is obtained. Furthermore, it is imperative that the guidance pertaining to accepting an LKD of childbearing age is both comprehensive and clear. We performed a systematic review with the aim of identifying all original research articles examining pregnancy outcomes, such as gestational hypertension and pre-eclampsia in LKD, and to compare the quality and consistency of the guidelines, consensus statements and expert opinions in this area. Method We searched Embase Ovid, MEDLINE Ovid, PubMed, society webpages and guideline registries for English-language publications published up until 18th December 2020. Article references and citation lists were also examined. The study was performed in accordance with the PRISMA guidelines. Results A total of 94 articles were screened. Nine cohort studies, two case reports, and one congress abstract were identified. The four most recent published papers were retrospective cohort studies, which included a combined number of 1,298 LKDs. All four studies reported an increase in both the relative and absolute risk of pregnancy-related complications. For example, the absolute risk of pre-eclampsia increased from ∼1-3% of pregnancies in LKD pre-donation to ∼4-6% of pregnancies in LKD post-donation. This meant that LKDs had a lower absolute risk of pre-eclampsia pre-donation, but after donation their risk of pre-eclampsia matched that of the general population. None of these studies distinguished between early or late onset pre-eclampsia. Participants were predominantly limited to Caucasian women. The lack of an ideal ‘living donor comparator group’ hindered a full quantification (including meta-analysis) of the pregnancy-related complications in LKD. We identified seven clinical guidelines and consensus statements published since 2010. These were broadly consistent in stating that the risk of pregnancy-related complications in LKD was similar to the general population’s risk, and that potential LKD should be informed of this risk. They were however inconsistent in their scope. For example, only three guidelines recommended enquiring into prior pregnancy-induced complications, and only two offered specific guidance on post-donation pregnancy follow-up. The most striking inconsistency was the differing view as to whether or not women who had not yet completed a family should be accepted as LKD. For example, one guideline stated that ‘women should not be excluded from donation solely on the basis of a desire to have children after donation’, whilst another stated that ‘it seem(ed) advisable to have completed a planned family before donation’. Conclusion The relative risk of pregnancy-related complications in LKD increases relative to the risk in non-LKD, though the increase in absolute risk remains very low. Though multiple guidelines for living kidney donation were identified, their advice for women of childbearing age was at times scant and inconsistent. The LKD of the future is likely to differ from the LKD of yesteryear. As such more focus should be placed on better identifying and individualising risk for LKD. Whilst the evidence suggests that an LKD’s risk of complications in pregnancy remains low post donation, one should keep in mind that a potential LKD’s personalised risk is unknown.

MosaicNeedles: A tool for multi-omic liquid biopsy sensitivity and specificity enhancement.

e15019 Background: Liquid biopsy has evolved to be an important method complementary to tissue biopsy. It is not only non-invasive, but also has the potential to detect cancer in its earliest stages and monitor patients in remission. The integration of proteomics into liquid biopsy may transform the molecular diagnostics of cancer and accelerate basic and clinical oncology research. A recent study showed that adding just 8 protein biomarkers to a panel of circulating DNA biomarkers increased the diagnostic accuracy up to 98% sensitivity and 99% specificity. Proteomics also bridges the gaps of functional information lost due to post-transcriptional and post-translational modifications in the genomic approach. However, the proteogenomic approach normally requires the use of multiple different assay technologies and laboratory workflows, including mass spectrometry. Methods: NanoMosaic’s Tessie platform employs a densely integrated nanoneedle sensor array (thus named MosaicNeedles) which can be used to detect both nucleic acids and proteins in a single assay process with reduced workflow complexity, without the need for mass spectrometry. Results: The NanoMosaic platform is a label-free, digital, single molecule counting technology using nanoneedles. It achieves sub-pg/ml (̃fM) level sensitivity with 7 logs of dynamic range. An array of nanoneedles is densely integrated and manufactured with CMOS-compatible nanofabrication processes. Each nanoneedle is a single molecule biosensor that is functionalized with capture probes. The capture probe can be either an antibody for protein detection or an oligonucleotide with a specific target sequence to a DNA fragment, mRNA, or miRNA of interest. The scattering spectrum of each nanoneedle changes when an analyte binds to its surface. At low abundance, analytes that are captured can be quantitated by counting the presence or absence of a color change on each individual nanoneedle in a binary fashion. As an analyte concentration increases the binding events increase accordingly and achieve saturation. In this range, an analog analysis on the spectrum shift will be performed, thus providing a wider dynamic range, up to 7 logs. Ultrahigh level multiplex can be achieved by parallelizing each analyte specific sensing area without loss of sensitivity or dynamic range. A 10,000-plex study can be achieved with a total of 2.5 billion nanoneedles on a 50mm by 50mm consumable. In this consumable, a 2,000-plex proteome and 8,000 cell-free DNA fragments can be detected. Conclusions: In conclusion, a full proteogenomic quantification can be performed on the NanoMosaic platform in one reaction, with higher sensitivity, lower cost and higher throughput than is currently possible by traditional methods. In addition, the high-plexibility of the NanoMosaic platform allows the discovery of new biomarkers across the whole proteome without the need for mass spectrometry.

Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities

Systemic risk, in a complex system with several interrelated variables, such as a financial market, is quantifiable from the multivariate probability distribution describing the reciprocal influence between the system’s variables. The effect of stress on the system is reflected by the change in such a multivariate probability distribution, conditioned to some of the variables being at a given stress’ amplitude. Therefore, the knowledge of the conditional probability distribution function can provide a full quantification of risk and stress propagation in the system. However, multivariate probabilities are hard to estimate from observations. In this paper, I investigate the vast family of multivariate elliptical distributions, discussing their estimation from data and proposing novel measures for stress impact and systemic risk in systems with many interrelated variables. Specific examples are described for the multivariate Student-t and the multivariate normal distributions applied to financial stress testing. An example of the US equity market illustrates the practical potentials of this approach.

Analysis of ZPPR-15 experimental data for VTR software validation

As part of the Versatile Test Reactor (VTR) program, extensive validation work is being performed to demonstrate the fidelity of the Argonne Reactor Code (ARC) software package presently used for the core design studies. It is anticipated that this software will be also used for initial operations although additional software may be introduced at a later time to supplement or replace some components. The validation work was focused on accumulating reactor models that are consistent with the VTR and thus can be used to validate the accuracy of predictions made by the software used to design and operate the VTR.The U.S. does not have any active experimental facilities that can neutronically mockup the VTR core design. However, in the past, the U.S. had extensive experimental facilities for fast reactor neutronics. In that regard, the ZPPR-15 experiments carried out at ANL during the 1980s work best because the fuel form is closest to the VTR design, there is a considerable amount of processed data available for use, and existing staff are already familiar with the experimental machine and measurements. Because of previous collaborative work, many of the ZPPR-15 loadings of interest have already been processed into MCNP and preliminary ARC models, and a full uncertainty quantification has already been completed for many of the ZPPR-15 measurements.This manuscript covers the recent analysis work on the ZPPR-15 experiments and compares it with the experimental results. With respect to the ARC software components, this manuscript focuses on validation of the MC2, DIF3D and GAMSOR using the ZPPR-15 experimental data.

Characterization of haustral activity in the human colon.

Contraction patterns of the human colon are rarely discussed from the perspective of its haustra. Colonic motility was analyzed in 21 healthy subjects using 84-sensor manometry catheters with 1-cm sensor spacing. Capsule endoscopy and manometry showed evidence of narrow rhythmic circular muscle contractions. X-ray images of haustra and sensor locations allowed us to identify manometry motor activity as intrahaustral activity. Two common motor patterns were observed that we infer to be associated with individual haustra: rhythmic pressure activity confined to a single sensor, and activity confined to a section of the colon of 3-6 cm length. Intrahaustral activity was observed by 3-4 sensors. Approximately 50% of the haustra were intermittently active for ∼30% of the time; 2,402 periods of haustral activity were analyzed. Intrahaustral activity showed rhythmic pressure waves, propagating in mixed direction, 5-30 mmHg in amplitude at a frequency of ∼3 cpm (range 2-6) or ∼12 cpm (range 7-15), or exhibiting a checkerboard segmentation pattern. Boundaries of the haustra showed rhythmic pressure activity with or without elevated baseline pressure. Active haustra often showed no boundary activity probably allowing transit to neighboring haustra. Haustral boundaries were seen at the same sensor for the 6- to 8-h study duration, indicating that they did not propagate, thereby likely contributing to continence. The present study elucidates the motility characteristics of haustral boundaries and the nature of intrahaustral motor patterns and paves the way for investigating their possible role in pathophysiology of defecation disorders.NEW & NOTEWORTHY Here, we present the first full characterization and quantification of motor patterns that we infer to be confined to single haustra, both intrahaustral activity and haustral boundary activity, in the human colon using high-resolution manometry. Haustral activity is intermittent but consistently present in about half of the haustra. Intrahaustral activity presents as a cyclic motor pattern of mixed propagation direction dominated by simultaneous pressure waves that can resolve into checkerboard segmentation, allowing for mixing, absorption, and stool formation.

Anion-exchange HPLC assay for separation and quantification of empty and full capsids in multiple adeno-associated virus serotypes

Gene therapy has entered a new era where numerous therapies for severe and rare diseases are generating robust and compelling clinical results. The rapid improvements in gene therapies over the past few years can be attributed to better scientific understanding of the critical quality attributes that contribute to a safe and efficacious product, as well as a better understanding of the manufacturing processes that are required to yield consistent products, which routinely meet the quality standards required for clinical studies. Of particular concern is the need for an effective, quality control (QC)-compatible, and versatile test method for the quantification of empty and full capsids in recombinant adeno-associated virus (rAAV) samples from multiple serotypes. In that regard, we describe the development of a QC-compatible anion-exchange chromatography method consisting of a modular discontinuous gradient to achieve full baseline peak separation and quantification of empty and full AAV capsids. Using an rAAV6 vector, our assay was shown to be precise, linear, robust, and accurate—correlating well with orthogonal methods such as analytical ultracentrifugation (AUC) and cryogenic transmission electron microscopy (Cryo-TEM). Additionally, we demonstrate the versatility of our approach by adapting the method to separate and quantify empty/full capsids in samples from several rAAV serotypes.

Validation of IRDFF-II library in VR-1 reactor field using thin targets

Cross section data are fundamental quantities which affect the accuracy of all calculations in nuclear applications. A new dosimetry library IRDFF-II that contains cross section evaluations with full uncertainty quantification was developed by the International Atomic Energy Agency and released in January 2020 (https://www-nds.iea.org/IRDFF). A previous version, IRDFF-1.05, was released in 2014 and experimental validation of the newly released cross section by spectrum averaged cross section (SACS) measurements is a high priority task. For such purpose, a neutron dosimeter set containing 5 target foils was activated in 2 independent experiments at the VR-1 reactor of the Czech Technical University in Prague. Care was taken to derive SACS with low uncertainties. New experimental evaluation method is in good agreement with previous approaches based on relative measurements using monitor foils. Good agreement of measured SACS and evaluated IRDFF-II cross sections is observed. Slight overestimation of evaluated ENDF/B-VIII.0 235U(nth,f) PFNS above 10 MeV is discussed.

Spatial factor modeling: A Bayesian matrix-normal approach for misaligned data.

Multivariate spatially oriented data sets are prevalent in the environmental and physical sciences. Scientists seek to jointly model multiple variables, each indexed by a spatial location, to capture any underlying spatial association for each variable and associations among the different dependent variables. Multivariate latent spatial process models have proved effective in driving statistical inference and rendering better predictive inference at arbitrary locations for the spatial process. High-dimensional multivariate spatial data, which are the theme of this article, refer to data sets where the number of spatial locations and the number of spatially dependent variables is very large. The field has witnessed substantial developments in scalable models for univariate spatial processes, but such methods for multivariate spatial processes, especially when the number of outcomes are moderately large, are limited in comparison. Here, we extend scalable modeling strategies for a single process to multivariate processes. We pursue Bayesian inference, which is attractive for full uncertainty quantification of the latent spatial process. Our approach exploits distribution theory for the matrix-normal distribution, which we use to construct scalable versions of a hierarchical linear model of coregionalization (LMC) and spatial factor models that deliver inference over a high-dimensional parameter space including the latent spatial process. We illustrate the computational and inferential benefits of our algorithms over competing methods using simulation studies and an analysis of a massive vegetation index dataset.

The Mg isotope signature of marine Mg-evaporites

Marine Mg-evaporites are a small oceanic sink of magnesium, precipitating only from extremely evaporated brines. The isotopic composition of Mg in seawater, δ26Mgseawater, has recently been shown to be an effective tool for reconstructing the Mg budget of the modern and past oceans. However, estimations of the Mg isotope fractionation between the Mg-evaporites and their precipitating solution are required for full quantification of the isotope effect of the evaporitic sink on δ26Mgseawater, as well as for utilizing ancient evaporitic sequences as an archive for past δ26Mgseawater. Here, we estimate the Mg isotope fractionation between Mg-evaporites and modern marine-derived brine along the course of seawater evaporation, up to degree evaporation of >200. The sequence of Mg-salts included epsomite (MgSO4·7H2O), kainite (KMgClSO4·3H2O), carnallite (KMgCl3·6H2O), kieserite (MgSO4·H2O) and bischofite (MgCl2·6H2O).The following isotope fractionation values, either negative or positive, were calculated from the isotope difference between the salt and its precipitating brine, and from the evolution of δ26Mg in the brine throughout the evaporation: Δcarnallite-brine = +1.1‰, Δepsomite-brine = +0.59‰, Δbischofite-brine = +0.33‰, Δkieserite-brine = −0.2‰ and Δkainite-brine = −1.3‰. Magnesium isotopic compositions determined on minerals from different ages in the geological record corroborate well these results. Due to precipitation of multi-mineral assemblages having isotope fractionation values of opposing signs, the δ26Mg value of the brine changes only slightly (<0.5‰) throughout the evaporation path, despite the considerable Mg removal (>50%). The isotope fractionations are shown to correlate with the number of water molecules coordinated to the Mg2+ and with Mg-O bond length in the mineral lattice.Given these isotope fractionations, it is calculated that a volume of 0.4 · 106–0.8 · 106 Km3 of a mono-mineral assemblage of kainite or carnallite needs to precipitate in order to change seawater δ26Mg by only 0.1‰. This huge volume is by far larger than the volume of these minerals known to date in the global geological record. Therefore, it is concluded that the impact of Mg-evaporites formation on δ26Mgseawater has been insignificant since the Proterozoic. The results of this study suggest that the Mg isotopic composition of Mg-evaporites preserved in the geological record of evaporitic basins may be used to: 1) quantify geochemical processes that fractionate Mg-isotopes within these basins, such as dolomitization; and 2) complete the secular variations curve of the marine δ26Mg record using basins with well-established evaporitic sequences.

A Study of the Centuries-Long Reliance on Local Ceramics in Jerash Through Full Quantification and Simulation

The Danish-German Jerash Northwest Quarter Project revealed a robust and striking pattern of the extreme dominance (>99%) of locally produced ceramics over six centuries and across different depositional contexts (in total over half a million pottery sherds). The archaeology of Jerash points towards an exceptional degree of self-sufficiency in craft products: why? The project team implemented a full quantification approach during excavation, manually and digitally recording and counting all pottery and other classes of artefacts. This enabled a full analysis of trends in production and use of ceramics throughout the archaeologically documented history of Jerash and revealed the unexpected pattern of the extreme dominance of local pottery. Archaeologists formulated a set of hypotheses to explain this pattern, and we developed an agent-based model of simple customer preference driving product distribution to evaluate several explanatory factors and their potential interactions. Our simulation results reveal that preference for locally produced ceramics at Jerash might be a plausible theory, but only if its intrinsic value was considered rather high in comparison to other goods, or if it was preferred by a majority of the population, and there was a tendency to follow this majority preference (or a combination of these factors). Here, we present a complete research pipeline of a full quantification of ceramics, analysis and modelling applicable at any archaeological site. We argue that transparent methods are necessary at all stages of an archaeological project: not only for data collection, management and analysis but also in theory development and testing. By focusing on a common archaeological material and by leveraging a range of widely available computational tools, we are able to better understand local and intra-regional distribution patterns of craft products in Jerash and in the ancient eastern Mediterranean.

Trends in ceramic assemblages from the Northwest Quarter of Gerasa/Jerash, Jordan

Past peoples left behind preciously little for us to reconstruct their daily lives and histories. However, some types of archaeological material stand the test of time better than others thanks to their durability and ubiquity, and foremost among them: the ceramic evidence. Pottery often serves as a proxy for the reconstruction of a variety of aspects of a past life: the wealth of city inhabitants, the function of spaces (Smith, 1987), cultural transmission between craftsmen (Coto-Sarmiento et al., 2018) or trading interactions (Brughmans and Poblome, 2016). However, to take full advantage of the wealth of information that can be derived from pottery evidence it is essential to use robust analytic methods establishing the full distribution in ceramic use and discard and its evolution over time.Here, we present a comprehensive data analysis of ceramic material coming from the Northwest Quarter of Jerash, present-day Jordan. It is one of the first examples of full quantification of an archaeological site of this age in the region. More than 625 000 pieces of pottery have been collected, recorded and analysed. We describe the process of data preparation, cleaning, exploratory analysis and statistical examination as well as visualisation. All steps of data analysis have been undertaken in the Python scripting environment making the process entirely transparent, reproducible and reusable for other researchers. We showcase how full quantification combined with quantitative analysis can lead to detection of significant trends in pottery evolution over centuries and enable robust comparative studies in the region and beyond.

Direct full quantification of the left ventricle via multitask regression and classification

Left ventricle (LV) quantitative indices, such as the areas of the cavity and myocardium, dimensions of the cavity, regional wall thickness, and phase classification, play a vital role in assessing the overall and local cardiac function in clinical practice. However, due to variations in the cardiac structures of different subjects and the inconvenience of manual feature extraction, it is difficult to accurately estimate left ventricle indices. To solve the above problem, this paper presents a multitask regression and classification network (MTRC-net) based on image representation to improve the accuracy of the quantified results. The network employs an autoencoder network to perform image representation, and then the representative images are fed into the multitask regression and classification network to extract the temporal and spatial features of the cardiac images, which can better help the MTRC-net to learn the potential image features, reduce the experimental errors and speed up the convergence of the model. In the multitask regression and classification network, a pseudo-3D network is used to compress the network model, and the circular hypothesis is applied to fully obtain the temporal and spatial information of the cardiac images. The proposed algorithm was verified on a dataset that includes images of 145 patients from the left ventricle quantification competition of MICCAI2018, and the results are as follows. The average absolute error of MAE in the Areas (cavity area and myocardium area) is 106 ± 88 mm2, the average absolute error of MAE in the Dims (directional dimensions of the cavity) region is 1.54 ± 1.40 mm, the average absolute error of MAE in the RWTs(regional wall thicknesses region) is 0.96 ± 0.70 mm, and the error rate of phase classification is 1.2%. The errors of each index are almost always smaller than those of the indices of the existing methods, and the experimental results illustrate the feasibility and effectiveness of the proposed method.

Reasoning and learning with context logic

Context logic (CL), a logical language similar in style to description logics but with a more cognitive motivation as a logical language of cognition, was developed since 2007 to provide a new approach to the symbol grounding problem, a key problem for reliable intelligent environments and other intelligent sensory systems. CL is a three-layered integrated hierarchy of languages: a relational base layer with the expressiveness of propositional logic (CLA), a quantifier-free decidable language (CL0), and an expressive language with full quantification (CL1). As was shown in 2018, the core CLA reasoning can be implemented on a variant of Kanerva’s Vector Symbolic Architecture, the activation bit vector machine (ABVM), shedding new light on the fundamental cognitive faculties of symbol grounding and imagery, but the system raised two questions: first, the core reasoning algorithm was a classical EXPTIME reasoner; second, fundamental aspects for a learning algorithm were sketched but not presented with a full algorithm. This paper addresses those two questions. We present a probabilistic linear time algorithm for reasoning over conjunctive normal form (CNF) CLA formulae together with a dual probabilistic linear time algorithm for learning CLA statements by collecting experienced snapshots in a disjunctive normal form (DNF).

MEASUREMENT OF INTEGRAL CROSS SECTIONS OF SELECTED DOSIMETRY REACTIONS IN LR-0 REACTOR

The cross section is a fundamental quantity which affects the accuracy of Monte Carlo simulations widely used in nuclear applications. A new dosimetry library IRDFF-II that contains cross section evaluations that include full uncertainty quantification is being developed by the International Atomic Energy Agency and expected to be released in January 2020; a preliminary version IRDFF-1.05 was released in 2014 and is being tested in this work. Validation of the cross-section evaluations proposed for this library is a high priority task. The validation can be realized using integral cross sections measured in standard and/or reference neutron benchmark fields. Integral quantities feature significantly lower uncertainties than differential nuclear data. If the neutron spectrum where the cross section is measured is well characterized, then the Spectrum Averaged Cross Section can be used for validating of existing evaluations.

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. We also provide extensive simulation results to demonstrate the validity of the proposed approach.

Associations of automatically segmented enlarged perivascular spaces with neuropathology and cognitive decline in a community cohort of older adults

AbstractBackgroundEnlarged perivascular spaces (EPVS) have been linked to increased risk of stroke, lower cognitive function, and vascular dementia. However, neuropathologic correlates of EPVS, and their contributions to cognition, are unclear. In this work, a deep‐learning algorithm for automatic segmentation of EPVS was developed, and allowed full quantification of EPVS in the whole brain and regionally. Then, neuropathologic correlates of EPVS and contributions of EPVS to cognition were analyzed within each region in a large community‐based cohort of older adults.MethodThis work included 287 participants of two longitudinal studies, Rush Memory and Aging Project, and Religious Orders Study (Figure 1). Ex‐vivo T2‐weighted images, and detailed neuropathologic examination by a board‐certified neuropathologist (blinded to all data) were obtained for one brain hemisphere from each participant. A total of 10 neuropathologies were assessed (Figure 2).The data from 10 participants with varying EPVS severity and manually segmented EPVS were used to train a deep learning model (Figure 3) to automatically segment EPVS in the ex‐vivo MRI data of all participants.Relative EPVS volume (EPVSrel), meaning EPVS volume normalized by white matter volume, and total number of EPVS (EPVSnum) was calculated for each participant. Similar measurements were made per lobe and basal ganglia (e.g. EPVSnum‐parietal). All measures were square root transformed to reduce skewness.ResultThe deep learning model achieved a mean Dice Similarity Coefficient of 0.655 ± 0.058 (comparison in Figure 4). Whole brain relative EPVS volume and number were associated with gross infarcts (Figure 5). Regional EPVS were associated with gross infarcts in most of the brain, and with cerebral amyloid angiopathy mainly in the occipital and temporal lobes (Figure 5). Relative EPVS volume in the occipital lobe was negatively correlated with semantic memory (‐0.443, p = 0.03) and perceptual speed (‐0.311, p = 0.048) above and beyond what was explained by neuropathologies and demographics.ConclusionThis investigation provides robust evidence on the neuropathologic correlates of EPVS and the contributions of EPVS on cognition in a large community‐based cohort of older adults. Fully quantitative assessment of EPVS was facilitated by automatic EPVS segmentation using deep learning. EPVS were shown to have associations with gross infarcts and cerebral amyloid angiopathy, and independent contributions on cognition.