Estimates In Order Research Articles

Transcriptomics profiling of the non-small cell lung cancer microenvironment across disease stages reveals dual immune cell-type behaviors.

Lung cancer is the leading cause of cancer death worldwide, with poor survival despite recent therapeutic advances. A better understanding of the complexity of the tumor microenvironment is needed to improve patients' outcome. We applied a computational immunology approach (involving immune cell proportion estimation by deconvolution, transcription factor activity inference, pathways and immune scores estimations) in order to characterize bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from patients across disease stages. Focusing specifically on early stage samples, we validated our findings using an independent LUAD cohort with 70 bulk RNAseq and 15 scRNAseq datasets and on TCGA datasets. Through our methodology and feature integration pipeline, we identified groups of immune cells related to disease stage as well as potential immune response or evasion and survival. More specifically, we reported a duality in the behavior of immune cells, notably natural killer (NK) cells, which was shown to be associated with survival and could be relevant for immune response or evasion. These distinct NK cell populations were further characterized using scRNAseq data, showing potential differences in their cytotoxic activity. The dual profile of several immune cells, most notably T-cell populations, have been discussed in the context of diseases such as cancer. Here, we report the duality of NK cells which should be taken into account in conjunction with other immune cell populations and behaviors in predicting prognosis, immune response or evasion.

Hydrodynamic limit for the non-cutoff Boltzmann equation

This work deals with the non-cutoff Boltzmann equation for all types of potentials, in both the torus \mathbf{T}^{3} and in the whole space \mathbf{R}^{3} , under the incompressible Navier–Stokes scaling. We first establish the well-posedness and decay of global mild solutions to this rescaled Boltzmann equation in a perturbative framework, that is, for solutions close to the Maxwellian, obtaining in particular integrated-in-time regularization estimates. We then combine these estimates with spectral-type estimates in order to obtain the strong convergence of solutions to the non-cutoff Boltzmann equation towards the incompressible Navier–Stokes–Fourier system.

Tahiti Population (Re)Estimates and Ideologies

The question of Tahiti population at contact was first addressed by assessing the reliability of early and later estimates, and then by a retrodiction based on the first reliable censuses and data on trends. Cook’s famous estimate of 204,000 Tahitians, affected by a wrong number of districts, has been reworked based on his own and the Forsters’ observations, using coast length and the age and sex structure of Tahiti population at contact in relation to infanticide, resulting in a range of 156,000-188,000 persons in 1774, before Boenechea’s flu in 1772 and the 1774 flu epidemics. The discrepancies between Wilson’s 16,050 and other missionaries’ estimate of 50,000 as of 1797 are puzzling, but the 1829-1830 missionary estimates were strongly affected by Tahitians’ resistance to Christianization. The twentieth century estimates are affected by a political context related to colonization - decolonisation, leading to the fact that researchers favour low estimates. McArthur uses missionary data, assuming that baptisms are births, although before the conversion was completed, baptisms included adults and children of all ages. She bases her estimate of 30,000 Tahitians at contact in 1767 on Wilson’s number of 16,050, who applied the size of nuclear households, 6 persons, to clans. Oliver followed her, however not excluding numbers twice as high or more. Our retrodiction on the 1848 and 1881 censuses, based on qualitative missionary reports on sanitary situation and data of Rarotonga that experienced similar events as Tahiti, yield, with conservative assumptions, 180,000 persons at contact, showing consistent results with early navigators’ estimates. My own PhD experience in the late 1980s shows that scholars’ re-estimates were constrained, directly or indirectly, by political considerations related to the guilt syndrome and colonialist ideology. This appears in the frequent contradictions found in their works to support low estimates in order to reduce the decline, despite evidence of dramatic epidemics and a steady decline extending almost throughout the 19th century. The same attitude of denial is found in some missionary, administrative and medical reports, particularly on the Marquesas in the late 19th century. Colonisation was associated with various ideologies, related to anthropology’s early attempts based on theories of evolutionism, functionalism and structuralism, occasionally resulting in fictions such as “primitive thinking”, “ahistorical societies” and “collective unconscious”, that were part of the justification of colonisation and its mistakes.

Transparency, institutions, and investment: The case of the extractive industries transparency initiative (EITI)

Since the establishment of the Extractive Industries Transparency Initiative (EITI), many resource-rich countries have joined international transparency initiatives. Considering that resource-rich countries have historically preferred opacity, this trend is surprising. Once a country joins the EITI, the country reports on its revenues, contracts, and licenses related to natural resources. The disclosure of information on resource revenues could threaten the survival of the leadership in less transparent democratic and autocratic regimes. In the face of the threats posed by enhanced transparency, these countries maintain their EITI membership, but for what reason? Using newly released data, this study argues that countries participate in international transparency initiatives to signal a favorable economic environment to international investors. This study demonstrates that under the self-reporting mechanism, investors reward or punish a country according to the level of its EITI membership. Based on an empirical analysis of 128 countries using two-stage least squares regression and the instrumental variable estimation in order to deal with selection bias and endogeneity (2002–2015), this study finds that joining in the EITI has a stronger positive effect on the inflow of foreign direct in-vestment (FDI) in less transparent countries, than in more transparent ones.

A multiproxy approach for the feeding ecology of Late Miocene ground sloths from the southwestern Amazonia, Upper Solimões Formation, Brazil

A rich assemblage of fossil sloths is recognized in the southwestern Brazilian Amazon, Upper Solimões Formation (Acre Basin), and represents some of the most diverse Folivora fauna from the Late Miocene of tropical South America. Here, we have used a multiproxy approach using carbon (δ13Csc) and oxygen (δ18Osc) isotope compositions of bioapatite structural carbonate, stereomicrowear, ecomorphological indices (relative muzzle width, RMW and occlusal surface area, OSA), and body mass estimates in order to suggest the habitats and diet of the mylodontids (Octodontobradys puruensis, Urumacotherium campbelli, Pseudoprepotherium venezuelanum and Mylodontinae gen. et. sp. nov.) and the nothrotheriid (cf. Mionothropus) ground sloths from localities along the banks at the Purus and Acre rivers in Amazonas and Acre states. The isotopic results (δ13Csc = −13.2 ± 1.2 ‰; δ18Osc = +24.7 ± 0.8 ‰) indicated a consistently C3 plant diet in open-canopy woodland and closed-canopy forest habitats during warm conditions across the Tortonian ages (∼10 and 8.5 Myr). U. campbelli (Urumacotherium sp., bm: 605 kg; Niterói Site) and cf. Mionothropus (bm: 163 kg; Acre 5 Site) possibly had specialized browsing habits (folivorous/frugivorous), due to the few coarse scars in relatively polished tooth surfaces and elongate skull with narrow muzzle, respectively. Both taxa, as also P. venezuelanum (bm: 733 kg; Niterói Site), showed a low OSA values suggesting limited oral food processing offset by higher fermentation capacity and/or lower metabolic requirements. Efficient capacity for oral food processing and a generalized browsing habit with consumption of high intrinsic toughness and abrasiveness items were inferred for O. puruensis (bm: 526 kg; Talismã Site), resembling the pattern observed for the Pleistocene megathere Eremotherium laurillardi from the southwestern Amazon (Acre State). A non-selective browser diet was also supported for the Mylodontinae gen. et. sp. nov. specimen from Cavalcante Site; its similar dentary morphology to the bulk-feeder taxa implies that this individual could occasionally eat C3 herbaceous/grasses.

Bayesian image segmentation under varying blur with triplet Markov random field

Abstract In this paper, we place ourselves in the context of the Bayesian framework for image segmentation in the presence of varying blur. The proposed approach is based on Triplet Markov Random Fields (TMRF). This method takes into account, during segmentation, peculiarities of an image such as noise, blur, and texture. We present an unsupervised TMRF method, which jointly deals with the problem of segmentation, and that of depth estimation in order to process fluorescence microscopy images. In addition to the estimation of the depth maps using the Metropolis-Hasting and the Stochastic Parameter Estimation (SPE) algorithms, we also estimate the model parameters using the SPE algorithm. We compare our TMRF method to other MRF models on simulated images, and to an unsupervised method from the state of art on real fluorescence microscopy images. Our method offers improved results, especially when blur is important.

Selecting the best solar EUV proxy for long-term timescale applications

Solar EUV proxies are a solution to the difficulties in obtaining solar EUV time series covering extended periods or during times without measurements available. Despite being very similar at interannual and decadal timescales, proxies are not identical. The upper atmosphere, highly sensitive to this spectral range, can serve as a “witness” of solar EUV variations to help selecting the best proxy. Its choice can be critical depending on the problem we are trying to solve through the proxy implementation and on the nature of the proxies’ non-identical part variation. We analyze the best proxy selection process whose outcome depends on the method and on the physics knowledge we have of the system. This study aims to contribute and highlight certain critical aspects of selection methodologies. A case where this issue becomes critical is upper atmosphere long-term trends’ estimation in order to detect the increasing greenhouse gas effect along the last decades.

Intelligent automatic operational modal analysis

This work presents a novel methodology for the automatic output-only modal identification of linear structures under ambient vibrations named Intelligent Automatic Operational Modal Analysis (i-AOMA). The proposed methodology leverages the Covariance-based Stochastic Subspace (SSI-cov) algorithm for output-only modal parameter identification and encompasses two key phases. In the first phase, the SSI-cov algorithm is executed using quasi-randomly samples of the control parameters. The corresponding stabilization diagrams are next processed via Kernel Density Estimation in order to prepare a training database for the i-AOMA's intelligent core. This is a machine learning technique (namely a Random Forest algorithm) that predicts the optimal combinations of the control parameters involved in the SSI-cov algorithm. The second phase of i-AOMA rests on the iterative generation of quasi-random control parameter samples. If a sample is classified as feasible by the intelligent core of i-AOMA, then the SSI-cov algorithm is applied, while a new sample is considered otherwise. Such iterative procedure stops once a statistical convergence criterion is fulfilled. Hence, ultimate modal estimates are carried out from the stabilization diagrams, and relevant statistics are computed to quantify the effects of the uncertainties attributable to the variability of the control parameters. The entire Python source code for i-AOMA is made freely available through public web repositories. Some applications of the proposed methodology are finally illustrated.

Late Holocene vegetation dynamics: degree and regional patterns of the Dark Ages woodland regeneration (ad 300–700) in the Netherlands

AbstractDuring the Dark Ages, which include the Late Roman period (ad 270–450) and the Early Middle Ages (ad 450–1050), large-scale vegetation development in northwest Europe was characterised by widespread regeneration of woodlands. This regeneration phase represents a break from the extensive reduction in woodland from human activities in late Holocene vegetation history. In the Netherlands, possible causes for this reversal can be found in a decreased human impact on the landscape, a colder and possibly wetter climate, and geomorphological changes. This paper presents a synthesis from 38 pollen records from a range of sites across the Netherlands, from which regional differences in the degree of woodland regeneration and vegetation composition before and during the Dark Ages have been identified. Both original data as well as vegetation abundance modelled by REVEALS were used to assess the regrowth of the woodland cover. The observed trends were considered in terms of the landscape setting and population estimates in order to disentangle the relative importance of these forcing factors. The results indicate that landscape and human impact were the most important factors determining the vegetation structure and changes to it. The pollen data show that in the Netherlands, the clearance of woodland in the Roman period followed by its regrowth afterwards were greatest in the river area. This can be linked to a high population density there during the Roman period followed by a strong decrease of population in the Dark Ages and to increased flooding.

Large deviations for the Yule–Walker estimator of near critical autoregressive processes

The large deviation principle is established for the Yule–Walker estimator of the near critical order one autoregressive process. The rate function is identified explicitly. Our result shows that, at the exponential scale, one cannot distinguish between near critical and the critical Yule–Walker estimators.

Quantile estimation of heterogenous panel quantile model with group structure

This paper introduces a heterogeneous panel quantile model whose coefficients have group structure. We propose a penalized minimum weighted absolute deviation estimator in order to estimate the coefficients and identify the group structure simultaneously at any quantiles.

Order estimation for autoregressive models using criteria based on stochastic complexity

In this paper, we are interested in the order estimation of an autoregressive model using the information criterion developed by El Matouat and Hallin (1996), which is based on stochastic complexity. This criterion is a generalization of the Hannan and Quinn criterion and provides a convergence of the model order estimator, but it depends on a parameter that is sensitive to the sample size. In order to select the exact order of the candidate model, we propose a method for identifying the values of this parameter from the sample using the information contained in sub-samples of increasing size. To study the performance of the proposed method in comparison with the usual criteria, we simulated samples from autoregressive models on which we applied our procedure. Simulation results support the relevance of our procedure when compared to the Akaike criterion, the Hannan and Quinn criterion, and the Schwarz criterion.

Benchmarking Bayesian quantum estimation

The quest for precision in parameter estimation is a fundamental task in different scientific areas. The relevance of this problem thus provided the motivation to develop methods for the application of quantum resources to estimation protocols. Within this context, Bayesian estimation offers a complete framework for optimal quantum metrology techniques, such as adaptive protocols. However, the use of the Bayesian approach requires extensive computational resources, especially in the multiparameter estimations that represent the typical operational scenario for quantum sensors. Hence, the requirement to characterize protocols implementing Bayesian estimations can become a significant challenge. This work focuses on the crucial task of robustly benchmarking the performances of these protocols in both single and multiple-parameter scenarios. By comparing different figures of merits, evidence is provided in favor of using the median of the quadratic error in the estimations in order to mitigate spurious effects due to the numerical discretization of the parameter space, the presence of limited data, and numerical instabilities. These results, providing a robust and reliable characterization of Bayesian protocols, find natural applications to practical problems within the quantum estimation framework.

On the Moldovan Economy’s Potential Output and Output Gap: Evidence from the Hodrick-Prescott Filter

This paper introduces the notion of an economy’s potential output, its output gap and its output gap rate, analyses the properties of the Hodrick-Prescott filter drawing from the studies in the literature, and proceeds to estimate and discuss Moldova’s potential output, its output gap and its output gap rate utilising a methodology which can be used in other economies. The data for our empirical work comprise annual data on: 1. Moldova’s nominal GDP and the annual rate of growth or real GDP over the period 2010-2022; and 2. The latest forecasts of these indicators for the period 2023-2026. Data earlier than 2010 are not comparable as they were compiled under a different methodology. We use this data set to generate a time series of the evolution of Moldova’s real GDP in average prices of 2010 over the period 2010-2026. We apply the Hodrick-Prescott filter with the two different values of the filter’s smoothing parameter suggested by the studies in the literature for the decomposition of annual data. As it is well-known that the estimates of the filter are sensitive to the value of the smoothing parameter, we use the average of the two estimates in order to calculate the Moldovan economy’s potential output, output gap and output gap rate through time. Our calculations suggest that, over the period analysed, Moldova’s potential output has grown at a modest rate which is not sufficient for the country to converge to the EU average and attain its EU aspirations. The paper concludes by discussing a number of recent economic developments in Moldova, the required policy response to attain Moldova’s EU aspirations and areas for further work.

Transporting results in an observational epidemiology setting: purposes, methods, and applied example.

In the medical domain, substantial effort has been invested in generating internally valid estimates in experimental as well as observational studies, but limited effort has been made in testing generalizability, or external validity. Testing the external validity of scientific findings is nevertheless crucial for the application of knowledge across populations. In particular, transporting estimates obtained from observational studies requires the combination of methods for causal inference and methods to transport the effect estimates in order to minimize biases inherent to observational studies and to account for differences between the study and target populations. In this paper, the conceptual framework and assumptions behind transporting results from a population-based study population to a target population is described in an observational setting. An applied example to life-course epidemiology, where internal validity was constructed for illustrative purposes, is shown by using the targeted maximum likelihood estimator.

Cortico-basal ganglia white matter microstructure is linked to restricted repetitive behavior in autism spectrum disorder

BackgroundRestricted repetitive behavior (RRB) is one of two behavioral domains required for the diagnosis of autism spectrum disorder (ASD). Neuroimaging is widely used to study brain alterations associated with ASD and the domain of social and communication deficits, but there has been less work regarding brain alterations linked to RRB.MethodsWe utilized neuroimaging data from the National Institute of Mental Health Data Archive to assess basal ganglia and cerebellum structure in a cohort of children and adolescents with ASD compared to typically developing (TD) controls. We evaluated regional gray matter volumes from T1-weighted anatomical scans and assessed diffusion-weighted scans to quantify white matter microstructure with free-water imaging. We also investigated the interaction of biological sex and ASD diagnosis on these measures, and their correlation with clinical scales of RRB.ResultsIndividuals with ASD had significantly lower free-water corrected fractional anisotropy (FAT) and higher free-water (FW) in cortico-basal ganglia white matter tracts. These microstructural differences did not interact with biological sex. Moreover, both FAT and FW in basal ganglia white matter tracts significantly correlated with measures of RRB. In contrast, we found no significant difference in basal ganglia or cerebellar gray matter volumes.LimitationsThe basal ganglia and cerebellar regions in this study were selected due to their hypothesized relevance to RRB. Differences between ASD and TD individuals that may occur outside the basal ganglia and cerebellum, and their potential relationship to RRB, were not evaluated.ConclusionsThese new findings demonstrate that cortico-basal ganglia white matter microstructure is altered in ASD and linked to RRB. FW in cortico-basal ganglia and intra-basal ganglia white matter was more sensitive to group differences in ASD, whereas cortico-basal ganglia FAT was more closely linked to RRB. In contrast, basal ganglia and cerebellar volumes did not differ in ASD. There was no interaction between ASD diagnosis and sex-related differences in brain structure. Future diffusion imaging investigations in ASD may benefit from free-water estimation and correction in order to better understand how white matter is affected in ASD, and how such measures are linked to RRB.

Set-Membership State Estimation for Multirate Nonlinear Complex Networks Under FlexRay Protocols: A Neural-Network-Based Approach.

In this article, the set-membership state estimation problem is investigated for a class of nonlinear complex networks under the FlexRay protocols (FRPs). In order to address practical engineering requirements, the multirate sampling is taken into account which allows for different sampling periods of the system state and the measurement. On the other hand, the FRP is deployed in the communication network from sensors to estimators in order to alleviate the communication burden. The underlying nonlinearity studied in this article is of a general nature, and an approach based on neural networks is employed to handle the nonlinearity. By utilizing the convex optimization technique, sufficient conditions are established in order to restrain the estimation errors within certain ellipsoidal constraints. Then, the estimator gains and the tuning scalars of the neural network are derived by solving several optimization problems. Finally, a practical simulation is conducted to verify the validity of the developed set-membership estimation scheme.

CreativeSeg: Semantic Segmentation of Creative Sketches.

The problem of sketch semantic segmentation is far from being solved. Despite existing methods exhibiting near-saturating performances on simple sketches with high recognisability, they suffer serious setbacks when the target sketches are products of an imaginative process with high degree of creativity. We hypothesise that human creativity, being highly individualistic, induces a significant shift in distribution of sketches, leading to poor model generalisation. Such hypothesis, backed by empirical evidences, opens the door for a solution that explicitly disentangles creativity while learning sketch representations. We materialise this by crafting a learnable creativity estimator that assigns a scalar score of creativity to each sketch. It follows that we introduce CreativeSeg, a learning-to-learn framework that leverages the estimator in order to learn creativity-agnostic representation, and eventually the downstream semantic segmentation task. We empirically verify the superiority of CreativeSeg on the recent "Creative Birds" and "Creative Creatures" creative sketch datasets. Through a human study, we further strengthen the case that the learned creativity score does indeed have a positive correlation with the subjective creativity of human. Codes are available at https://github.com/PRIS-CV/Sketch-CS.

Fractional order for Multi-criteria Control Performance Assessment

This paper presents an alternative way to do control performance assessment (CPA). The task to measure controller quality has to meet contradictory goals. Generally, the tuning of any controller means reaching a compromise between the accuracy and speed. The demanded ratio between them depends on the process requirements, technological limitations and just the engineering skills. Two fundamental indexes, like the overshoot and settling time fit perfectly into the picture. Considered investigations follow this research path, but with the use of modern measures: L-moments, tail index and ARFIMA filter fractional order estimator. The assessment uses two dimensional Index Ratio Diagrams (IRD), which allow to present and compare contradictory measures in a single diagram. They also allow to define new multi-criteria index able to compare different loops. The validation is compared against commonly used integral measures: means square and absolute errors.

Improved exponential type ratio estimator in double sampling for stratification

The objective of this research is to create a chain-ratio-type exponential estimator in order to estimate the finite population mean in double sampling for stratification. An estimator for population mean has been constructed based on the concept of chain-ratio estimators. The constructed estimator is compared to the standard unbiased estimator, as well as the other relevant existing estimators and conditions are shown to yield better results in terms of efficiency. To support the theoretical results the study has been done on both natural as well as simulated populations.