Dense Sampling Research Articles

Orchid phylogenetics and evolution: history, current status and prospects.

Orchidaceae are one of the two largest families of angiosperms; they exhibit a host of changes -- morphological, ecological and molecular -- that make them excellent candidates for evolutionary study. Such studies are most effectively performed in a phylogenetic context, which provides direction to character change. Understanding of orchid relationships began in the pre-evolutionary classification systems of the 1800's that were based solely on morphology, and now is largely based on genomic analysis. The resulting patterns have been used to update family classification and to test many evolutionary hypotheses in the family. Recent analyses with dense sampling and large numbers of nuclear loci have yielded well-supported trees that have confirmed many longstanding hypotheses and overturned others. They are being used to understand evolutionary change and diversification in the family. These include dating the origination of the family, analysis of change in ecological habit (from terrestrial to epiphytic and back again in some cases), revealing significant plastid genome change in leafless holomycotrophs, studying biogeographic patterns in various parts of the world, and interpreting patterns of fungal associations with orchids. Understanding of orchid relationships has progressed significantly in recent decades, especially since DNA sequence data have been available. These data have contributed to an increasingly refined classification of orchids and the pattern has facilitated many studies on character evolution and diversification in the family. Whole genome studies of the family are just beginning and promise to reveal fine-level details underlying structure and function in these plants, and, when set in a phylogenetic context, provide a much richer understanding of how the family has been so successful in diversification.

Tutorial on Molecular Latent Space Simulators (LSSs): Spatially and Temporally Continuous Data-Driven Surrogate Dynamical Models of Molecular Systems.

The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources. Provided the training data provide sufficient sampling of the relevant thermodynamic states and dynamical transitions to robustly learn the underlying microscopic propagator, an LSS furnishes a global model of the dynamics capable of producing temporally and spatially continuous molecular trajectories. Trained LSS models have produced simulation trajectories at up to 6 orders of magnitude lower cost than standard MD to enable dense sampling of molecular phase space and large reduction of the statistical errors in structural, thermodynamic, and kinetic observables. The LSS employs three deep learning architectures to solve three independent learning problems over the training data: (i) an encoding of the high-dimensional MD into a low-dimensional slow latent space using state-free reversible VAMPnets (SRVs), (ii) a propagator of the microscopic dynamics within the low-dimensional latent space using mixture density networks (MDNs), and (iii) a generative decoding of the low-dimensional latent coordinates back to the original high-dimensional molecular configuration space using conditional Wasserstein generative adversarial networks (cWGANs) or denoising diffusion probability models (DDPMs). In this software tutorial, we introduce the mathematical and numerical background and theory of LSS and present example applications of a user-friendly Python package software implementation to alanine dipeptide and a 28-residue beta-beta-alpha (BBA) protein within simple Google Colab notebooks.

Lower levels of household transmission of SARS-CoV-2 VOC Omicron compared to Wild-type: an interplay between transmissibility and immune status.

Knowledge of SARS-CoV-2 household transmission dynamics guides infection control and vaccination measures. This household cohort study prospectively assessed the impact of both the Omicron BA.2 variant and immunity on household transmission using dense saliva sampling and sequence analysis. Households consisting of a PCR-confirmed index and at least two household members were enrolled in March and April 2022 during the Omicron BA.2 wave in the Netherlands. SARS-CoV-2 PCR was performed on ten consecutive saliva samples. Serum-antibodies were measured at baseline and day 42. Household and per-person Secondary Attack Rate (SAR) were calculated to measure transmission. Whole genome sequencing was performed for phylogenetic analysis, followed by sensitivity analysis, to correct for multiple household introductions and index definition. Results were compared with the identical, early-pandemic, pre-immunisation predecessor study. Sixty-seven households were included, consisting of 241 individuals (median age 33.0 years (IQR 12.0-46.0)). Maximum household SAR was 59.7%, per-person SAR 41.5%. Paediatric index cases were more likely to transmit. Transmission was negatively affected by household members' immunity. Phylogenetic analysis showed multiple introductions in four households. Sensitivity analysis resulted in a minimal household SAR of 51.0% and per-person SAR of 28.5%. The Omicron BA.2 variant is highly transmissible within households. However, the transmission rate is lower compared to previous studies with other SARS-CoV-2 variants, highlighting the effect of immunity. Regardless of immune status, children have a crucial role in Omicron household transmission. Intensive sampling and phylogenetic analysis are beneficial for correctly calculating transmission rates, especially during periods of minimal behavioural restrictions.

Formation of Chemically Complex Intergranular Glass Film: An Effective Strategy to Hinder Grain Coarsening

Thermally driven grain coarsening is a commonly encountered issue in nanocrystalline ceramics, particularly in high‐temperature environments. The intergranular glass film (IGF) constitutes a crucial component of most ceramics and plays a pivotal role in the process of grain coarsening. In this study, it is proposed to impede grain coarsening by constructing a chemically complex IGF comprising multiple dopants with distinct ionic radii. Ternary dopants encompassing Al3+, Y3+, and La3+ ions are simultaneously incorporated into a ZrO2–SiO2 nanocomposite. To fabricate the nanocomposite, an amorphous precursor powder with uniformly dispersed dopants is prepared using a chemical coprecipitation method, followed by rapid hot pressing to obtain a dense bulk sample. The distribution behavior of ternary dopants at IGFs between adjacent ZrO2 nanocrystallites (NCs) is carefully examined. It is revealed that the ternary dopants coexist at the IGFs. Moreover, Si4+ ions exhibit preferential enrichment at the IGFs. Remarkably, the presence of chemically complex IGFs significantly enhances the resistance to grain coarsening in ZrO2 NCs up to 1000 °C. In these findings, valuable insights are offered for designing and fabricating nanocomposites with exceptional resistance against grain coarsening.

Hot Uniaxial Pressing and Pressureless Sintering of AlCrCuFeMnNi Complex Concentrated Alloy—A Comparative Study

External pressure is often applied during sintering to obtain materials with improved properties. For complex concentrated alloys (CCAs), this processing step is commonly performed in vacuum. However, this can promote the evaporation of elements and increase the oxide content, thereby degrading the properties of the alloy. In this study, we compared the microstructures and properties of AlCrCuFeMnNi CCA samples obtained by hot uniaxial pressing sintering (HPS) and pressureless sintering (PLS) using a helium atmosphere purified by an oxygen getter system. The powders were prepared from mixtures of CrFeMn, AlNi and Cu and sintered by HPS at 900 °C for 1 h with an applied pressure of 30 MPa and by PLS at 1050 °C for 1 h. The samples were characterised using X-ray diffraction, scanning and transmission electron microscopy, energy-dispersive X-ray spectroscopy, electron backscattering diffraction, density measurements and hardness tests. It was found that the oxygen getter system promoted oxygen partial pressure values at sintering temperatures similar to those of a mixture of 90% helium and 10% hydrogen. The HPS allowed us to obtain almost fully dense samples with a smaller average grain size and finer distribution of aluminium oxides than PLS. These differences increased the hardness of the samples sintered under pressure.

Imaging system high dynamic range colorimetric calibration method based on a digital chain

Research on high dynamic range (HDR) color management imposes critical requirements on calibration methods between imaging systems and standard radiation. This paper proposes a colorimetric calibration method based on digital chain measurement for imaging systems. First, a HDR colorimetric calibration process model for imaging systems is constructed based on an imaging chain. It includes a light source, target reflectance, optical system parameters, spectral sensitivities, and color matching functions. Subsequently, visual tristimulus values and three-channel response values of an imaging system are obtained using the proposed model in response to the same target, and the target characteristic parameters are adjusted to simulate different HDR imaging scenarios. Following that, various regression algorithms can be employed for HDR colorimetric calibration of imaging systems. The experimental findings demonstrate that the method proposed in this paper boasts a broader dynamic range and denser sampling, thereby enhancing the accuracy of colorimetric characterization models and achieving superior resolution in color measurement.

Functional Principal Component Analysis for Continuous Non-Gaussian, Truncated, and Discrete Functional Data.

Mobile health studies often collect multiple within-day self-reported assessments of participants' behavior and well-being on different scales such as physical activity (continuous scale), pain levels (truncated scale), mood states (ordinal scale), and the occurrence of daily life events (binary scale). These assessments, when indexed by time of day, can be treated and analyzed as functional data corresponding to their respective types: continuous, truncated, ordinal, and binary. Motivated by these examples, we develop a functional principal component analysis that deals with all four types of functional data in a unified manner. It employs a semiparametric Gaussian copula model, assuming a generalized latent non-paranormal process as the underlying generating mechanism for these four types of functional data. We specify latent temporal dependence using a covariance estimated through Kendall's bridging method, incorporating smoothness in the bridging process. The approach is then extended with methods for handling both dense and sparse sampling designs, calculating subject-specific latent representations of observed data, latent principal components and principal component scores. Simulation studies demonstrate the method's competitive performance under both dense and sparse sampling designs. The method is applied to data from 497 participants in the National Institute of Mental Health Family Study of Mood Spectrum Disorders to characterize differences in within-day temporal patterns of mood in individuals with the major mood disorder subtypes, including Major Depressive Disorder and Type 1 and 2 Bipolar Disorder. Software implementation of the proposed method is provided in an R-package.

Phylogenomics and phylogeographic model testing using convolutional neural networks reveal a history of recent admixture in the Canarian Kleinia neriifolia.

Multiple-island endemics (MIE) are considered ideal natural subjects to study patterns of island colonization that involve recent population-level genetic processes. Kleinia neriifolia is a Canarian MIE widespread across the archipelago, which exhibits a close phylogenetic relationship with species in northwest Africa and at the other side of the Sahara Desert. Here, we used target sequencing with plastid skimming (Hyb-Seq), a dense population-level sampling of K. neriifolia, and representatives of its African-southern Arabian relatives to infer phylogenetic relationships and divergence times at the species and population levels. Using population genetic techniques and machine learning (convolutional neural networks [CNNs]), we reconstructed phylogeographic relationships and patterns of genetic admixture based on a multilocus SNP nuclear dataset. Phylogenomic analysis based on the nuclear dataset identifies the northwestern African Kleinia anteuphorbium as the sister species of K. neriifolia, with divergence starting in the early Pliocene. Divergence from its sister clade, comprising species from the Horn of Africa and southern Arabia, is dated to the arid Messinian period, lending support to the climatic vicariance origin of the Rand Flora. Phylogeographic model testing with CNNs supports an initial colonization of the central island of Tenerife followed by eastward and westward migration across the archipelago, which resulted in the observed east/west phylogeographic split. Subsequent population extinctions linked to aridification events, and recolonization from Tenerife, are proposed to explain the patterns of genetic admixture in the eastern Canary Islands. We demonstrate that CNNs based on SNPs can be used to discriminate among complex scenarios of island migration and colonization.

Real-Time Tracking Target System Based on Kernelized Correlation Filter in Complicated Areas.

The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target's two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience.

Trajectory optimization and obstacle avoidance of autonomous robot using Robust and Efficient Rapidly Exploring Random Tree.

One of the key challenges in robotics is the motion planning problem. This paper presents a local trajectory planning and obstacle avoidance strategy based on a novel sampling-based path-finding algorithm designed for autonomous vehicles navigating complex environments. Although sampling-based algorithms have been extensively employed for motion planning, they have notable limitations, such as sluggish convergence rate, significant search time volatility, a vast, dense sample space, and unsmooth search routes. To overcome the limitations, including slow convergence, high computational complexity, and unnecessary search while sampling the whole space, we have proposed the RE-RRT* (Robust and Efficient RRT*) algorithm. This algorithm adapts a new sampling-based path-finding algorithm based on sampling along the displacement from the initial point to the goal point. The sample space is constrained during each stage of the random tree's growth, reducing the number of redundant searches. The RE-RRT* algorithm can converge to a shorter path with fewer iterations. Furthermore, the Choose Parent and Rewire processes are used by RE-RRT* to improve the path in succeeding cycles continuously. Extensive experiments under diverse obstacle settings are performed to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach outperforms existing methods in terms of computational time, sampling space efficiency, speed, and stability.

Development of a 7000 series aluminium alloy suitable for laser-based Additive Manufacturing

High strength aluminium alloys are of significant interest for laser-based Additive Manufacturing as they can provide a high specific strength but have limited assembly possibilities due to poor weldability and the presence of large intermetallic particles challenging the conventional manufacturing route. Increasing the geometrical freedom by enabling their use with laser-based Additive Manufacturing would create new possibilities. This study investigates the possibilities of using an aluminium 7000 series alloy with Laser Powder Bed Fusion (L-PBF) and secondly for comparison with Direct Energy Deposition (DED). The chemical composition targets 5.1–6.1 wt% Zn and 2.1–3.9 wt% Mg. The level of Zn and Mg in the powder was increased to 10 wt% Zn and 3 wt% Mg to compensate for the expected evaporation during the laser-based AM process and guarantee a sufficient Zn and Mg content for precipitation strengthening. Solidification cracking in the initial alloy composition was resolved by the addition of 1 wt% Zr and 1 wt% V. For L-PBF, a study of the impact of the laser spot size showed a larger spot size is beneficial to improve the process stability. A study of the capability of preheating and of slowing down the process showed both further improve the process stability as well which is required to manufacture successfully larger parts to characterise the quasi-static tensile properties. For optimal process parameters for a fast process, meaning a scan speed of 700 mm/s, a yield stress of 425 ± 7 MPa and an ultimate tensile strength of 442 ± 5 MPa was obtained after a homogenisation and ageing heat treatment. In the as built state, the strength was significantly lower. For optimal process parameters for a slow process, meaning a scan speed of 250 mm/s, a yield stress of 398 ± 11 MPa and an ultimate tensile strength of 440 ± 16 MPa were obtained in as built state. For DED the mechanical properties of the alloy showed to be significantly dependent on process parameters. An excessive energy input results in more Zn and Mg evaporation and decreased cooling rates. Such parameters also impact the process stability of DED throughout the build cycle. Limiting the energy input resulted in dense samples and stable build cycles which after a heat treatment to peak ageing reached a yield stress of around 400 MPa and an ultimate tensile stress of around 440 MPa in the X- and Z-direction.

Detection of the large-scale tidal field with galaxy multiplet alignment in the DESI Y1 spectroscopic survey

ABSTRACT We explore correlations between the orientations of small galaxy groups, or ‘multiplets’, and the large-scale gravitational tidal field. Using data from the Dark Energy Spectroscopic Instrument (DESI) Y1 survey, we detect the intrinsic alignment (IA) of multiplets to the galaxy-traced matter field out to separations of $100\,h^{-1}$ Mpc. Unlike traditional IA measurements of individual galaxies, this estimator is not limited by imaging of galaxy shapes and allows for direct IA detection beyond redshift $z=1$. Multiplet alignment is a form of higher order clustering, for which the scale-dependence traces the underlying tidal field and amplitude is a result of small-scale ($\lt 1h^{-1}$ Mpc) dynamics. Within samples of bright galaxies, luminous red galaxies (LRG) and emission-line galaxies, we find similar scale-dependence regardless of intrinsic luminosity or colour. This is promising for measuring tidal alignment in galaxy samples that typically display no IA. DESI’s LRG mock galaxy catalogues created from the A bacusS ummitN-body simulations produce a similar alignment signal, though with a 33 per cent lower amplitude at all scales. An analytic model using a non-linear power spectrum (NLA) only matches the signal down to 20 $h^{-1}$ Mpc. Our detection demonstrates that galaxy clustering in the non-linear regime of structure formation preserves an interpretable memory of the large-scale tidal field. Multiplet alignment complements traditional two-point measurements by retaining directional information imprinted by tidal forces, and contains additional line-of-sight information compared to weak lensing. This is a more effective estimator than the alignment of individual galaxies in dense, blue, or faint galaxy samples.

Biogeographic shifts in the microbial co-occurrence network features of three domains across complex environmental gradients in subtropical coastal waters

BackgroundBacteria, Archaea, and Microeukaryotes comprise taxonomic domains that interact in mediating biogeochemical cycles in coastal waters. Many studies have revealed contrasting biogeographic patterns of community structure and assembly mechanisms in microbial communities from different domains in coastal ecosystems; however, knowledge of specific biogeographic patterns on microbial co-occurrence relationships across complex coastal environmental gradients remains limited. Using a dense sampling scheme at the regional scale, SSU rRNA gene amplicon sequencing, and network analysis, we investigated intra- and inter-domain co-occurrence relationships and network topology-based biogeographic patterns from three microbial domains in coastal waters that show environmental gradients across the inshore-nearshore-offshore continuum in the East China Sea.ResultsOverall, we found the highest complexity and connectivity in the bacterial network, the highest modularity in the archaeal network, and the lowest complexity, connectivity, and modularity in the microeukaryotic network. Although microbial co-occurrence networks from the three domains showed distinct topological features, they exhibited a consistent biogeographic pattern across the inshore-nearshore-offshore continuum. Specifically, the nearshore zones with intermediate levels of terrestrial impacts reflected by multiple environmental factors (including water temperature, salinity, pH, dissolved oxygen, and nutrient-related parameters) had a higher intensity of microbial co-occurrence for all three domains. In contrast, the intensity of microbial co-occurrence was weaker in both the inshore and the offshore zones at the two ends of the environmental gradients. Archaea occupied a central position in the microbial inter-domain co-occurrence network. In particular, members of the Thaumarchaeota Marine Group I (MGI, now placed within the Family Nitrosopumilaceae of the Phylum Thermoproteota) appeared to be the hubs in the biogeographic shift between inter-domain network modules across environmental gradients.ConclusionsOur work offers new insights into microbial biogeography by integrating network features into biogeographic patterns, towards a better understanding of the potential of microbial interactions in shaping biogeographic patterns of coastal marine microbiota.

Gradient-enhanced deep Gaussian processes for multifidelity modeling

Multifidelity models integrate data from multiple sources to produce a single approximator for the underlying process. Dense low-fidelity samples are used to reduce interpolation error, while sparse high-fidelity samples are used to compensate for bias or noise in the low-fidelity samples. Deep Gaussian processes (GPs) are attractive for multifidelity modeling as they are non-parametric, robust to overfitting, perform well for small datasets, and, critically, can capture nonlinear and input-dependent relationships between data of different fidelities. Many datasets naturally contain gradient data, most commonly when they are generated by computational models that have adjoint solutions or are built in automatic differentiation frameworks.Principally, this work extends deep GPs to incorporate gradient data. We demonstrate this method on an analytical test problem and two realistic aerospace problems: one focusing on a hypersonic waverider with an inviscid gas dynamics truth model and another focusing on the canonical ONERA M6 wing with a viscous Reynolds-averaged Navier-Stokes truth model.In both examples, the gradient-enhanced deep GP outperforms a gradient-enhanced linear GP model and their non-gradient-enhanced counterparts.

Low-power adaptive sampling electronic nose system with a Radon transform-based convolutional neural network for optimized gas recognition

The electronic nose (E-nose) systems provide great promise in capturing information about the identity of gaseous chemical analytes as well as their temporal variation. However, the regular and dense sampling events of the E-nose often lead to a substantial amount of redundancy in the temporal structure, which in turn limits their application in efficiency-sensitive scenarios such as advanced robotics and the Internet of Things. Herein, we introduce a novel operating mechanism for E-noses, which dynamically adjusts the time interval of operation to reduce power consumption and information content through an adaptive sampling mechanism. Three strategic schemes with different operating principles are presented to implement this mechanism: the accurate scheme targets the optimized discrimination accuracy, the vague scheme is oriented toward power-sensitive scenarios, and the balanced scheme aims to balance cost and benefit. In order to ensure that the adaptively sampled inhomogeneous data can be compatible with each other, we further propose an algorithm based on the Radon transform that can convert non-uniform time series with timestamp information into the same size tensor. Finally, we designed a convolutional neural network-based classification model for gaseous chemical analytes, providing guidance on scheme and parameter selection with an accuracy of up to 99 % and power savings of up to 96 %. Overall, this work provides a novel solution to optimize temporal redundancy in E-nose systems and a generalized approach to the corresponding deep-learning data processing.

Accurate calibration spectra for precision radial velocities

Astronomical spectrographs require calibration of their dispersion relation, for which external sources like hollow-cathode lamps or absorption-gas cells are useful. Laser frequency combs (LFCs) are often regarded as ideal calibrators because they provide the highest accuracy and dense sampling, but LFCs are facing operational challenges such as generating blue visual light or tunable offset frequencies. As an example of an external source, we aim to provide a precise and accurate frequency solution for the spectrum of molecular iodine absorption by referencing to an LFC that does not cover the same frequency range. We used a Fourier Transform Spectrometer (FTS) to produce a consistent frequency scale for the combined spectrum from an iodine absorption cell at 5200– 6200 Å and an LFC at 8200 Å. We used 17 807 comb lines to determine the FTS frequency offset and compared the calibrated iodine spectrum to a synthetic spectrum computed from a molecular potential model. In a single scan, the frequency offset was determined from the comb spectrum with an uncertainty of ∼1 cms−1. The distribution of comb line frequencies is consistent with no deviation from linearity. The iodine observation matches the model with an offset of smaller than the model uncertainties of ∼1 m s−1, which confirms that the FTS zero point is valid outside the range covered by the LFC, and that the frequencies of the iodine absorption model are accurate. We also report small systematic effects regarding the iodine model’s energy scale. We conclude that Fourier Transform Spectrometry can transfer LFC accuracy into frequency ranges not originally covered by the comb. This allows us to assign accurate frequency scales to the spectra of customized wavelength calibrators. The calibrators can be optimized for individual spectrograph designs regarding resolution and spectral bandwidth, and requirements on their long-term stability are relaxed because FTS monitoring can be performed during operation. This provides flexibility for the design and operation of calibration sources for high-precision Doppler experiments.

Far-field radiation patterns of distributed acoustic sensing in anisotropic media with an explosive source and vertically straight fiber

Distributed acoustic sensing (DAS) is increasingly used in seismic exploration owing to its wide frequency range, dense sampling and real-time monitoring. DAS radiation patterns help to understand angle response of DAS records and improve the quality of inversion and imaging. In this paper, we solve the 3D vertical transverse isotropic (VTI) Christoffel equation and obtain the analytical, first-order, and zero-order Taylor expansion solutions that represent P-, SV-, and SH-wave phase velocities and polarization vectors. These analytical and approximated solutions are used to build the P/S plane-wave expression identical to the far-field term of seismic wave, from which the strain rate expressions are derived and DAS radiation patterns are thus extracted for anisotropic P/S waves. We observe that the gauge length and phase angle terms control the radiating intensity of DAS records. Additionally, the Bond transformation is adopted to derive the DAS radiation patterns in title transverse isotropic (TTI) media, which exhibits higher complexity than that of VTI media. Several synthetic examples demonstrate the feasibility and effectiveness of our theory.

A unique interplay of access and selection shapes peritoneal metastasis evolution in colorectal cancer.

Whether metastasis in humans can be accomplished by most primary tumor cells or requires the evolution of a specialized trait remains an open question. To evaluate whether metastases are founded by non-random subsets of primary tumor lineages requires extensive, difficult-to-implement sampling. We have realized an unusually dense multi-region sampling scheme in a cohort of 26 colorectal cancer patients with peritoneal metastases, reconstructing the evolutionary history of on average 28.8 tissue samples per patient with a microsatellite-based fingerprinting assay. To assess metastatic randomness, we evaluate inter- and intra-metastatic heterogeneity relative to the primary tumor and find that peritoneal metastases are more heterogeneous than liver metastases but less diverse than locoregional metastases. Metachronous peritoneal metastases exposed to systemic chemotherapy show significantly higher inter-lesion diversity than synchronous, untreated metastases. Projection of peritoneal metastasis origins onto a spatial map of the primary tumor reveals that they often originate at the deep-invading edge, in contrast to liver and lymph node metastases which exhibit no such preference. Furthermore, peritoneal metastases typically do not share a common subclonal origin with distant metastases in more remote organs. Synthesizing these insights into an evolutionary portrait of peritoneal metastases, we conclude that the peritoneal-metastatic process imposes milder selective pressures onto disseminating cancer cells than the liver-metastatic process. Peritoneal metastases' unique evolutionary features have potential implications for staging and treatment.

Spark plasma sintering of Ruddlesden–Popper Ca2−xDyxMnO4 ceramics: Impact on thermoelectric and mechanical performance

Abstractn‐type Dy‐doped Ca2MnO4 powders with the Ruddlesden–Popper structure were synthesized by the combustion method and sintered by conventional sintering and spark plasma sintering (SPS). Conventional sintering fails to densify Ca2MnO4, but the addition of Li2O proves to be a highly effective sintering additive, albeit with a detrimental impact on electrical conductivity. In contrast, SPS at 850°C for 1 min yields nanostructured, nearly fully dense samples, demonstrating superior outcomes. The dimensionless figure of merit ZT reaches 0.02 at 750°C. Additionally, the best sample exhibits a remarkable hardness value of 10.7 GPa, underlining the excellent mechanical properties achieved through this innovative approach.

A Rapid Thermal‐Radiation‐Assisted Sintering Strategy for Nd–Fe–B‐Type Magnets

The green transition has spiked demand for high‐performance sintered Nd–Fe–B permanent magnets, necessitating advanced powder consolidation technologies to enhance production efficiency. This study explores the rapid sintering methodology for an Nd–Fe–B powder using a radiation‐assisted sintering approach. The case study material is an industrially used powder, prepared through strip‐casting, hydrogen decrepitation, and jet milling, with a mean particle size of 5.5 μm. The powder is sintered to full density in a modified spark plasma sintering furnace, achieving pressureless conditions and eliminating electrical currents in the sample to preserve grain alignment and prevent decomposition of the hard‐magnetic phase. Fully dense samples are obtained with heating rates ranging from 10 to 200 °C min−1 and up to 5 min of dwell time at 1100 °C. Rapid heating results in grain size and microstructure comparable to conventionally sintered magnets prepared from the same powder, without compromising magnetic performance after postsinter annealing at 520 °C for 120 min. This sintering method contributes to a novel strategy for optimizing magnet production by utilizing efficient thermal‐radiation heat transfer. The combination of rapid heating and pressureless sintering drastically reduces heat‐up and dwell times, providing a fundamental advantage over slow conventional sintering.