Sampling Resolution Research Articles

Theta oscillations optimize a speed-precision trade-off in phase coding neurons.

Theta-band oscillations (3-8 Hz) in the mammalian hippocampus organize the temporal structure of cortical inputs, resulting in a phase code that enables rhythmic input sampling for episodic memory formation and spatial navigation. However, it remains unclear what evolutionary pressures might have driven the selection of theta over higher-frequency bands that could potentially provide increased input sampling resolution. Here, we address this question by introducing a theoretical framework that combines the efficient coding and neural oscillatory sampling hypotheses, focusing on the information rate (bits/s) of phase coding neurons. We demonstrate that physiologically realistic noise levels create a trade-off between the speed of input sampling, determined by oscillation frequency, and encoding precision in rodent hippocampal neurons. This speed-precision trade-off results in a maximum information rate of ∼1-2 bits/s within the theta frequency band, thus confining the optimal oscillation frequency to the low end of the spectrum. We also show that this framework accounts for key hippocampal features, such as the preservation of the theta band along the dorsoventral axis despite physiological gradients, and the modulation of theta frequency and amplitude by running speed. Extending the analysis beyond the hippocampus, we propose that theta oscillations could also support efficient stimulus encoding in the visual cortex and olfactory bulb. More broadly, our framework lays the foundation for studying how system features, such as noise, constrain the optimal sampling frequencies in both biological and artificial brains.

Gaussian process regression as a powerful tool for analysing time series in environmental geochemistry

Monitoring programs require more advanced data management for the registered time series. Classical temporal series decomposition cannot fulfil current needs regarding adequate data representation, optimization of the spatial-temporal sampling resolution and predictive power. In the manuscript at hand, we will demonstrate that Gaussian process regression (GPR) models are a vital machine-learning tool to interpret temporal series, improving understanding of geochemical cycles, providing input data for geochemical models and acting as a guide for future decisions in environmental monitoring. Firstly, we explore the impacts of sampling frequency in the GPR performance for temporal series with variable lengths and sampled frequencies of water discharges. On a second approach, we present the strengths and weaknesses between classical decomposition of temporal series and GPR results for a case study: a 14-year record of water discharge, suspended particulate matter and antimony concentrations in the Garonne River. Our results suggest that (i) even short temporal series with low sampling resolution can be accurately characterized by GPR when presenting well defined seasonal patterns, and (ii) GPR provides more detailed and robust support than classical statistics to identify processes responsible for multi-scale geochemical signals. This work provides a reference for researchers, engineers, and stakeholders for more reliable monitoring, understanding, and managing aquatic ecosystems.

Radiographic characteristics and chronological patterns of transient apical breakdown (TAB) in luxated permanent teeth: A retrospective observational study.

To describe radiographic features, clinical signs and symptoms, and chronological patterns of post-traumatic transient apical breakdown (TAB) in luxated permanent teeth. Records from 56 patients treated at the Dental Trauma Clinic of the School of Dentistry of the Federal University of Minas Gerais from 1993 to 2024 were accessed to collect demographic, clinical, and imaging features of 89 teeth that presented with radiographic signs of TAB after traumatic dental injury (TDI). Kaplan-Meier curves were built to illustrate the time elapsed between trauma until TAB onset and resolution for the whole sample and for each one of the TAB patterns. A Cox regression was used to explore the effect of clinical covariates in both events. Patients' mean age at the time of trauma was 17.7 ± 6.6years (range 9.1 to 39.7years), with most being male (59.6%). All included teeth were mature at the time of trauma (Moorrees stage 6). Upper central incisors (66.3%) were predominant and mostly suffered extrusions (40.4%) or lateral luxations (32.6%). TAB was observed as an expansion of the periodontal ligament associated with a "V-shaped" widening of the apical foramen together with apical root resorption in 45 teeth (50.6%). Expansion of the periodontal ligament associated with "V-shaped widening of the apical foramen only was observed in 32 teeth (36%), and 12 teeth (13.5%) showed expansion of the periodontal ligament associated with apical root resorption only. The overall median time for TAB onset was 3.2months (range, 26days to 8months), being significantly lower for pattern 2 (1.8months) when compared to TAB pattern 1 and 3 (3.4, and 3.9months) respectively (Logrank test p values < 0.001 for both comparisons). The overall median time for TAB regression was 9.1months (range, 4.8 to 34.8months) after trauma. Again, TAB pattern 2 presented lower periods when compared to TAB pattern 1 and 3 (Logrank test p values < 0.001 and 0.013, respectively). Color changes were not frequent neither at the time of TAB onset nor at the time of TAB resolution. However, while most teeth (53.3%) did not respond to pulp sensibility tests at the time of TAB onset, this proportion decreased to 17.6% by the time of TAB resolution. TAB was mainly diagnosed within four months after luxation of mature teeth, most of them being resolved within the 1-year of follow-up. The most frequent TAB pattern was an expansion of the PDL associated with a "V-shaped" widening of the apical foramen together with external apical root resorption. These findings represent an important contribution for treatment decision-making during the follow-up of luxated permanent teeth.

Antenna pattern reconstruction for mid-band massive antenna array based on a single-cut field transformation algorithm in a compact multi-probe anechoic chamber setup

ABSTRACT It has become a common challenge on how to measure radiation patterns of massive antenna arrays working at high frequency with high efficiency and accuracy, which has attracted huge interest from both academia and industry. In this paper, a highly accurate and efficient far field (FF) pattern reconstruction strategy through conducting single cut near field (NF) to FF transformation algorithm based on spherical wave expansion (SWE) is proposed for fast measurements of large base stations (BSs) operating at mid-band. Our objective is to design a compact multi-probe setup to measure key radiation parameters of the massive array, with low-cost and high-efficiency. A BS of 16 × 16 elements operating at mid-band is adopted as the device under test (DUT) in this paper. Analysis is conducted on the impact of measurement distance, sampling range and sampling density on the reconstruction accuracy of the algorithm in the compact multi-probe anechoic chamber setup, demonstrating that the required key information of the main lobe of the DUT can be accurately reconstructed when the measurement range R = 1/20 FF distance, sampling resolution ∆θ = 3°, and sampling range θ ∈ [75°, 105°] are selected, respectively. The impact of phase center offset on the accuracy of FF pattern reconstruction is analyzed when only one sub-array is enabled, demonstrating the effectiveness of the algorithm for sub-array measurements.

Hyper‐Sampling Imaging by Measurement of Intra‐Pixel Quantum Efficiency Using Steady Wave Field

AbstractThe transition from optical film to digital image sensors (DIS) in imaging systems has brought great convenience to human life. However, the sampling resolution of DIS is considerably lower than that of optical film due to the limitation that the pixels are significantly larger than the silver halide molecules. How to break DIS's sampling limit and achieve high‐resolution imaging is highly desired for imaging applications. In the research, a novel mechanism that allows for a significant reduction in the smallest sampling unit of DIS to as small as 1/16th of a pixel, or even smaller, through measuring the intra‐pixel quantum efficiency for the first time and recomputing the image — a technique referred to as hyper‐sampling imaging (HSI) is developed. Employing the HSI method, the physical sampling resolution of regular DIS can be enhanced by 4 × 4 times or potentially higher, and detailed object information can be acquired. The HSI method has undergone rigorous testing in real‐world imaging scenarios, demonstrating its robustness and efficiency in overcoming the sampling constraints of conventional DIS. This advancement is particularly beneficial for applications such as remote sensing, long‐range reconnaissance, and astronomical observations, where the ability to capture fine details is paramount.

Simulation Study of High-Precision Characterization of MeV Electron Interactions for Advanced Nano-Imaging of Thick Biological Samples and Microchips.

The resolution of a mega-electron-volt scanning transmission electron microscope (MeV-STEM) is primarily governed by the properties of the incident electron beam and angular broadening effects that occur within thick biological samples and microchips. A precise understanding and mitigation of these constraints require detailed knowledge of beam emittance, aberrations in the STEM column optics, and energy-dependent elastic and inelastic critical angles of the materials being examined. This simulation study proposes a standardized experimental framework for comprehensively assessing beam intensity, divergence, and size at the sample exit. This framework aims to characterize electron-sample interactions, reconcile discrepancies among analytical models, and validate Monte Carlo (MC) simulations for enhanced predictive accuracy. Our numerical findings demonstrate that precise measurements of these parameters, especially angular broadening, are not only feasible but also essential for optimizing imaging resolution in thick biological samples and microchips. By utilizing an electron source with minimal emittance and tailored beam characteristics, along with amorphous ice and silicon samples as biological proxies and microchip materials, this research seeks to optimize electron beam energy by focusing on parameters to improve the resolution in MeV-STEM/TEM. This optimization is particularly crucial for in situ imaging of thick biological samples and for examining microchip defects with nanometer resolutions. Our ultimate goal is to develop a comprehensive mapping of the minimum electron energy required to achieve a nanoscale resolution, taking into account variations in sample thickness, composition, and imaging mode.

Agricultural spray drone deposition, Part 2: Operational height and nozzle influence pattern uniformity, drift, and weed control

Abstract Agricultural spray drone (ASD) use in managed turfgrass has been given limited attention in the scientific literature. Further, deposition patterns of ASD spray have been obscured in previous research by ambient wind, crop canopy interference, and limited sampling resolution. Using a continuous sampling method involving blue colorant and water sprayed over white Kraft paper that was assessed via digital image analysis of stain objects and referenced spectrophotometric analysis of extractant, deposition metrics were estimated across a 29.3-m transect perpendicular to an ASD or ground sprayer spray swath. The ASD applies very fine droplets that are highly concentrated with herbicide, similar to ultra-low volume treatments, that improved smooth crabgrass [Digitaria ischemum (Schreb.) Schreb. ex Muhl.] control compared to a ground sprayer when the ASD was operated at 2 m above the turf. Unfortunately, these very fine droplets also drift, leading to four times greater droplet density at distance of almost 12 m away from the targeted spray swath following an operational height of 10 m compared to that at 2 m. As ASD operational height increases, drift and effective swath width at 30% coefficient of variation uniformity increases while effective application rate, total deposition, and D. ischemum control by quinclorac herbicide decreased. Total deposition decreased 6% for each m increase in ASD operational height, likely due to evaporation. The potential losses due to evaporation is a serious consideration for ASD use that has received little attention in the scientific literature. Our data suggest that ASD operational height should be as low as possible, but modification of spray systems may be needed to improve homogeneity of spray pattern.

Separation of radioactive isotope 227Ac from LaBr3 via vacuum evaporation for low-background scintillating detectors

Both radioactive isotopes 138La and 227Ac in LaBr3:Ce crystal typically produce considerable intrinsic background signals, and thus hinder their applications with low count rates. Since the complete separation of 138La is almost impossible, the contamination of 138La is stubbornly present in all La-halide based scintillators. In this study, an efficient purification protocol based on vacuum distillation was developed to separate 227Ac from feedstock and reduce intrinsic background signal at 1.6–3 MeV introduced by 227Ac in LaBr3:Ce crystals. Through combined theoretical and experimental analysis, the alpha contamination from 227Ac in LaBr3: Ce was reduced exploiting the different evaporation behaviors of LaBr3 and AcBr3. Additional impurities were also reduced to lower levels, as confirmed by glow discharge mass spectrometry (GDMS). The purified material, distilled at various temperatures (1203 K, 1253 K, and 1303 K), was used to grow LaBr3:Ce single crystals for performance evaluation. The crystals distilled at 1203 K demonstrated enhanced scintillation performance, featuring lower background counts (0.0181 counts·s−1·cm−3 @1.6–3 MeV) by approximately 50%–60 % of untreated sample and excellent energy resolution (2.47 %@662 KeV). This study presents an effective approach for preparing low-background lanthanum bromide, so as to offer valuable insights for isotope separation in scintillation crystals by vacuum distillation.

Exploring the utility of complementary separations in liquid chromatography

An alternative strategy is explored for the separation of samples by liquid chromatography (LC). Unlike traditional approaches that aim to resolve all components in a given sample within a single LC separation, the proposed strategy uses two or more distinct separations carried out with a different gradient program and/or using different separation chemistries i.e., a different set of mobile and stationary phase. This set of complementary incomplete separations (CIS) is selected such that each component is at least fully resolved once, meaning the most critical pairs of each individual separation can be left unseparated. This allows for a significant time saving per separation. To investigate whether such an approach can lead to overall shorter analysis times than is possible with the fastest single-run gradient separation, a comprehensive in silico study covering a statistically significant number of samples is undertaken. The investigation shows that, for the presently considered sample sets and chemistries, CIS has a substantially higher probability, about two times greater for the simplest samples considered in this work and as much as 30 times greater for more complex samples, to fully resolve an unknown sample compared to a single gradient separation. Comparing separation speeds, the CIS approach can achieve complete sample resolution on average approximately four times faster than a single separation. Our findings thus demonstrate the potential of CIS in enhancing separation efficiency and offer insights regarding their use for solving analytical challenges.

Localization of albumin with correlative super resolution light- and electron microscopy in the kidney

The functioning of vertebrate life relies on renal filtration of surplus fluid and elimination of low-molecular-weight waste products, while keeping serum proteins in the blood. In disease, however, there is leak of serum proteins and tracing them to identify the leaking position within tissue with a nanometer resolution poses a significant challenge. Correlative microscopy integrates the specificity of fluorescent protein labeling into high-resolution electron micrographs. Using chemical tagging of albumin with synthetic fluorophores we achieve protein-specific labeling that preserve their post-embedding fluorescence after high-pressure freezing and freeze-substitution of murine kidney tissue. Using advanced registration techniques for super-resolution correlative light and electron microscopy, we can localize the labeled albumin with a high precision in the x-y plane of electron micrographs and cartograph its distribution. Thereby we can quantify the albumin concentration and measure a linear reduction gradient across the kidney filtration barrier. Our study shows the feasibility of combining different microscopy contrasts for tracing fluorescently labeled protein markers with super resolution in various tissue samples and opens new perspectives for correlative imaging in volume electron microscopy.

Realistic photon-number resolution in Gaussian boson sampling

Gaussian boson sampling (GBS) is a model of nonuniversal quantum computation that claims to demonstrate quantum supremacy with current technologies. This model entails sampling photocounting events from a multimode Gaussian state at the outputs of a linear interferometer. In this scheme, collision events—those with more than one photon for each mode—are infrequent. However, they are still used for validation purposes. Therefore, the limitation of realistic detectors to perfectly resolve adjacent photon numbers becomes pivotal. We derive a photocounting probability distribution in GBS schemes which is applicable for use with general detectors and photocounting techniques. This probability distribution is expressed in terms of functionals of the field-quadrature covariance matrix, e.g., Hafnian and Torontonian in the well-known special cases of photon-number-resolving and on-off detectors, respectively. Based on our results, we consider a GBS validation technique involving detectors with realistic photon-number resolution. Published by the American Physical Society 2024

No universal mathematical model for thermal performance curves across traits and taxonomic groups

In ectotherms, the performance of physiological, ecological and life-history traits universally increases with temperature to a maximum before decreasing again. Identifying the most appropriate thermal performance model for a specific trait type has broad applications, from metabolic modelling at the cellular level to forecasting the effects of climate change on population, ecosystem and disease transmission dynamics. To date, numerous mathematical models have been designed, but a thorough comparison among them is lacking. In particular, we do not know if certain models consistently outperform others and how factors such as sampling resolution and trait or organismal identity influence model performance. To fill this knowledge gap, we compile 2,739 thermal performance datasets from diverse traits and taxa, to which we fit a comprehensive set of 83 existing mathematical models. We detect remarkable variation in model performance that is not primarily driven by sampling resolution, trait type, or taxonomic information. Our results reveal a surprising lack of well-defined scenarios in which certain models are more appropriate than others. To aid researchers in selecting the appropriate set of models for any given dataset or research objective, we derive a classification of the 83 models based on the average similarity of their fits.

Autonomous data sampling for high-resolution spatiotemporal fish biomass estimates

Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R2 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ∼ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 sequential observations (>6 hours). However, we found that diel patterns in fish detection strongly affected the abundance estimates due to vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (r = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.

Effect of environmental DNA sampling resolution in detecting nearshore fish biodiversity compared to capture surveys.

Sampling and sequencing marine environmental DNA (eDNA) provides a tool that can increase our ability to monitor biodiversity, but movement and mixing of eDNA after release from organisms before collection could affect our inference of species distributions. To assess how conditions at differing spatial scales influence the inferred species richness and compositional turnover, we conducted a paired eDNA metabarcoding and capture (beach seining) survey of fishes on the coast of British Columbia. We found more taxa were typically detected using eDNA compared to beach seining. eDNA identified more taxa with alternative habitat preferences, and this richness difference was greater in areas of high seawater movement, suggesting eDNA has a larger spatial grain influenced by water motion. By contrast, we found that eDNA consistently missed low biomass species present in seining surveys. Spatial turnover of communities surveyed using beach seining differed from that of the eDNA and was better explained by factors that vary at small (10-1000s meters) spatial scales. Specifically, vegetation cover and shoreline exposure explained most species turnover from seining, while eDNA turnover was not explained by those factors and showed a distance decay pattern (a change from 10% to 25% similarity from 2 km to 10 km of distance), suggesting unmeasured environmental variation at larger scales drives its turnover. Our findings indicate that the eDNA sample grain is larger than that of capture surveys. Whereas seining can detect differences in fish distributions at scales of 10s-100s of meters, eDNA can best summarize fish biodiversity at larger scales possibly more relevant to regional biodiversity assessments.

Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy

By simultaneously achieving high spatial and angular sampling resolution, four dimensional scanning transmission electron microscopy (4D STEM) is enabling analysis techniques that provide great insight into the atomic structure of materials. Applying these techniques to scientifically and technologically significant beam-sensitive materials remains challenging because the low doses needed to minimise beam damage lead to noisy data. We demonstrate an unsupervised deep learning model that leverages the continuity and coupling between the probe position and the electron scattering distribution to denoise 4D STEM data. By restricting the network complexity it can learn the geometric flow present but not the noise. Through experimental and simulated case studies, we demonstrate that denoising as a preprocessing step enables 4D STEM analysis techniques to succeed at lower doses, broadening the range of materials that can be studied using these powerful structure characterization techniques.

LiveLattice: Real-time visualisation of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 × 1536 × 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data preprocessing, visualisation, and analysis on standard workstations, thereby revolutionising biological imaging applications for LLSM and similar microscopes.

Obtaining High‐Resolution Magnetic Records From Speleothems Using Magnetic Microscopy

AbstractSpeleothems are mineral deposits capable of recording detrital and/or chemical remanent magnetization at annual timescales. They can offer high‐resolution paleomagnetic records of short‐term variations in Earth's magnetic field, crucial for understanding the evolution of the dynamo. Owing to limitations on the magnetic moment sensitivity of commercial cryogenic rock magnetometers (∼10−11 Am2), paleomagnetic studies of speleothems have been limited to samples with volumes of several hundreds of mm3, averaging tens to hundreds of years of magnetic variation. Nonetheless, smaller samples (∼1–10 mm3) can be measured using superconducting quantum interference device (SQUID) microscopy, with a sensitivity better than ∼10−15 Am2. To determine the application of SQUID microscopy for obtaining robust high‐resolution records from small‐volume speleothem samples, we analyzed three different stalagmites collected from Lapa dos Morcegos Cave (Portugal), Pau d'Alho Cave (Brazil), and Crevice Cave (United States). These stalagmites are representative of a range of magnetic properties and have been previously studied with conventional rock magnetometers. We show that by using SQUID microscopy we can achieve a five‐fold improvement in temporal resolution for samples with higher abundances of magnetic carriers (e.g., Pau d'Alho Cave and Lapa dos Morcegos Cave). In contrast, speleothems with low abundances of magnetic carriers (e.g., Crevice Cave) do not benefit from higher resolution analysis and are best analyzed using conventional rock magnetometers. Overall, by targeting speleothem samples with high concentrations of magnetic carriers we can increase the temporal resolution of magnetic records, setting the stage for resolving geomagnetic variations at short time scales.

Source term estimation in the unsteady flow with dynamic mode decomposition

When estimating source parameters in the unsteady flow, the flow information of pollution dispersion is indispensable. It is common practice to save the flow information in the computer in advance but it requires large storage space. Besides, when contaminants are released after a time period of the flow field saved before, calculating the flow field by Computational Fluid Dynamics (CFD) model demands massive computational cost. Dynamic Mode Decomposition (DMD) is thereby proposed to solve the problems mentioned above. Firstly, the fields are decomposed by DMD. Then, the simulated concentrations are acquired by the adjoint equation based on the field synthesized by DMD. Finally, the measured concentrations and the simulated concentrations are taken into Bayesian inference to accomplish source term estimation (STE). The results show that the estimated results with high accuracy are obtained both in the reconstruction stage and in the prediction stage when using the fields obtained by DMD. Also, the efficiency of predicting the future flow by DMD is much higher than that by CFD simulation, suggesting that DMD can improve the efficiency of STE in some cases. As DMD uses a small number of dominant modes to synthesize the approximate fields with minor errors, it reduces the storage demand of flow information in STE. The sampling range and sampling resolution should be properly selected to ensure the accuracy of STE.

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data

BackgroundRNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level.ResultsHere, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets.ConclusionsScan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Sensitive response of erosion and weathering to the Indian Summer Monsoon changes in South Asia during Dansgaard-Oeschger oscillations

Continental weathering plays a key role in regulating the long-term climate stability via negative feedback. However, whether and how erosion and weathering respond to rapid climate change (e.g millennial scale) in large river basins remains unclear, partly due to the lack of archives with robust age models and high sampling resolution. As one of the largest sediment source-to-sink systems, the Himalaya-Ganga/Brahmaputra River-Bay of Bengal system is considered as an ideal laboratory for examining the weathering-climate relationships over the past. Here we present the elemental and mineralogical data from well age-constrained core sediments in the Bay of Bengal, which document the erosion and weathering conditions in South Asia during the last glacial period. All proxies generally show larger values during glacial interstadials than the stadials, consistent with the millennial variabilities of regional sea surface temperature (SST) and the Indian Summer Monsoon (ISM) strength. We confirm that the erosion and chemical weathering signals in Ganga/Brahmaputra River basins, which sensitively respond to the ISM strength and/or temperature change, could be propagated through the large river systems and faithfully preserved in the South Asian marginal seas, without noticeable time lag on a millennial scale. Our findings provide valuable data for response of erosion and weathering dynamics in a large river basin to rapid climate changes, and highlight the immense potential of monsoon-driven continental silicate weathering in floodplains as a substantial short-term carbon sink, thus underscoring a pivotal natural carbon sequestration mechanism amidst the escalating threat of global warming.