Quantitative Links Research Articles

Is it in their words? Teachers' enthusiasm and their natural language in class-A sentiment analysis approach.

Teacher enthusiasm is an undisputedly important characteristic of teachers, with demonstrated positive effects on student outcomes. Existing research typically operationalised teacher enthusiasm via trait-based teacher- or student ratings. Strikingly little is known about how teachers' trait enthusiasm manifests in their actual in-situ classroom behaviour. Some findings have been reported regarding teachers' nonverbal behaviours, but the links between teacher enthusiasm and teacher language are unknown so far. The present contribution fills this research gap by applying lexicon-based sentiment analysis to quantify teachers' emotional tone from transcribed teacher utterances obtained from video recordings of full mathematics lessons (45 min). N = 19 secondary school mathematics teachers and their N = 393 students participated in our study. We realised the sentiment analysis using Remus etal.'s emotion lexicon SentiWS (v2.0, 2019). We obtained both teacher self-reports and student ratings to assess teachers' enthusiasm, shown habitually (trait), and during the videotaped lesson (in situ). Regarding trait enthusiasm, teachers' own, but not the students', ratings were positively linked with teachers' verbally expressed sentiment in the videotaped lesson, specifically its positive valence. Regarding in-situ enthusiasm, associations were even larger but also did not reach significance for the student ratings. This is the first study to employ sentiment analysis on transcripts of German teachers' in-class talk. Besides the quantitative links between teacher enthusiasm and their language, it also provides qualitative insights on positive emotional teacher talk in mathematics.

Effect of polyacrylate polymer on the mechanical and water retention properties of cemented soil: A multiscale investigation

Granitic residual soils (GRS) occupy substantial deposits in many regions but exhibit deficiencies restricting direct engineering applications. This study investigated polyacrylate (PA) polymer treatment to enhance the mechanical and hydrological properties of GRS. Triaxial tests evaluated the effects of PA content (0–3 %) on shear strength parameters under varied confining pressures. Soil-water retention characteristics were determined using pressure plate and filter paper methods. Microstructural analyses via scanning electron microscopy and pore attribute quantification furnished insights. Results showed PA increased peak deviator stress and cohesion in a content-sensitive manner up to an optimum 2 % dosage. A modified Duncan-Chang model accurately described the nonlinear stress-strain behavior. PA amplified water retention across the entire suction domain by elevating the air entry value attributed to microstructural changes. Morphological analyses indicated PA formed binding membranes consolidating particles with redistributed pore attributes towards stabler, load-resisting configurations. Quantitative links validated hypothesized mechanisms whereby optimized pore architectures directly translated bulk fabric cohesion and friction enhancement. Multi-scale evidence synthesized a self-consistent microscopic mechanism of PA-mediated pore network reconstitution systematically strengthening the granular framework. This study demonstrated polyacrylate modification optimizes key engineering properties of naturally deficient GRS, offering design guidance for sustainable geotechnical applications exploiting abundant residual soil resources.

Scour-depth variability controls channel-scale stratigraphy in experimental braided rivers

ABSTRACT Braided rivers distribute sediment across landscapes, often forming wide channel belts that are preserved in stratigraphy as coarse-grained deposits. Theoretical work has established quantitative links between the depth distribution of formative channels in a braided river and the geometry of their preserved strata. However, testing these predictive relationships between geomorphic process and stratigraphic product requires examining how braided rivers and their deposits coevolve, with high resolution in both space and time. Here, using a series of four runs of a physical experiment, we examine the controls of water discharge and slope on the resulting geometry of preserved deposits. Specifically, we focus on how a twofold variation in water discharge and initial riverbed slope affects the spatiotemporal distribution of channel depths and the geometry of preserved deposits of a braided river. We find that the channel depths in the laboratory experiment are described by a two-parameter gamma distribution and the deepest scours correspond to zones of erosion at channel-belt margins and channel-thread confluences in the channel belt. We use a reduced-complexity flow model to reconstruct flow depths, which were shallower compared to channel thalweg depths. Synthetic stratigraphy built from timeseries of topographic surfaces shows that the distribution of cut-and-fill-unit thickness is invariant across the experiments and is determined by the variability in scour depths. We show that the distribution of cut-and-fill-unit thickness can be used to reconstruct formative-channel-depth distributions and that the mean thickness of these units is 0.31 to 0.62 times the mean formative flow depth across all experiments. Our results suggest that variations in discharge and slope do not translate to measurable differences in preserved cut-and-fill-unit thickness, suggesting that changes in external forcings are likely to be preserved in braided river deposits only when they exceed a certain threshold of change.

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data.

Tissue context and molecular profiling are commonly used measures in understanding normal development and disease pathology. In recent years, the development of spatial molecular profiling technologies (e.g. spatial resolved transcriptomics) has enabled the exploration of quantitative links between tissue morphology and gene expression. However, these technologies remain expensive and time-consuming, with subsequent analyses necessitating high-throughput pathological annotations. On the other hand, existing computational tools are limited to predicting only a few dozen to several hundred genes, and the majority of the methods are designed for bulk RNA-seq. In this context, we propose HE2Gene, the first multi-task learning-based method capable of predicting tens of thousands of spot-level gene expressions along with pathological annotations from H&E-stained images. Experimental results demonstrate that HE2Gene is comparable to state-of-the-art methods and generalizes well on an external dataset without the need for re-training. Moreover, HE2Gene preserves the annotated spatial domains and has the potential to identify biomarkers. This capability facilitates cancer diagnosis and broadens its applicability to investigate gene-disease associations. The source code and data information has been deposited at https://github.com/Microbiods/HE2Gene.

Effects of predation risk on parasite-host interactions and wildlife diseases.

Landscapes of fear can determine the dynamics of entire ecosystems. In response to perceived predation risk, prey can show physiological, behavioral, or morphological trait changes to avoid predation. This in turn can indirectly affect other species by modifying species interactions (e.g., altered feeding), with knock-on effects, such as trophic cascades, on the wider ecosystem. While such indirect effects stemming from the fear of predation have received extensive attention for herbivore-plant and predator-prey interactions, much less is known about how they alter parasite-host interactions and wildlife diseases. In this synthesis, we present a conceptual framework for how predation risk-as perceived by organisms that serve as hosts-can affect parasite-host interactions, with implications for infectious disease dynamics. By basing our approach on recent conceptual advances with respect to predation risk effects, we aim to expand this general framework to include parasite-host interactions and diseases. We further identify pathways through which parasite-host interactions can be affected, for example, through altered parasite avoidance behavior or tolerance of hosts to infections, and discuss the wider relevance of predation risk for parasite and host populations, including heuristic projections to population-level dynamics. Finally, we highlight the current unknowns, specifically the quantitative links from individual-level processes to population dynamics and community structure, and emphasize approaches to address these knowledge gaps.

Vesicular neurotransmitters exocytosis monitored by amperometry: theoretical quantitative links between experimental current spikes shapes and intravesicular structures.

Single cell amperometry has proven to be a powerful and well-established method for characterizing single vesicular exocytotic events elicited at the level of excitable cells under various experimental conditions. Nevertheless, most of the reported characteristics are descriptive, being mostly concerned with the morphological characteristics of the recorded current spikes (maximum current intensities, released charge, rise and fall times, etc.) which are certainly important but do not provide sufficient kinetic information on exocytotic mechanisms due to lack of quantitative models. Here, continuing our previous efforts to provide rigorous models rationalizing the kinetic structures of frequently encountered spike types (spikes with unique exponential decay tails and kiss-and-run events), we describe a new theoretical approach enabling a quantitative kinetic modeling of all types of exocytotic events giving rise to current spikes exhibiting exponential decay tails. This model follows directly from the fact that the condensation of long intravesicular polyelectrolytic strands by high concentrations of monocationic neurotransmitter molecules leads to a matrix structure involving two compartments in constant kinetic exchanges during release. This kinetic model has been validated theoretically (direct and inverse problems) and its experimental interest established by the analysis of the amperometric spikes relative to chromaffin and PC12 cells previously published by some of us.

A missing trophic link: Contribution of the microbial loop to the estimation of the trophic position of pelagic consumers

AbstractTrophic position estimation has been a central issue in food web studies. Estimates using compound‐specific isotopic analyses of amino acids were generally based on the trophic enrichment of glutamic acid. However, this trophic position estimation mainly accounts for the trophic steps involving metazoa, as the isotopic fractionation of this amino acid is minimal at trophic steps within the microbial food web. Alternatively, trophic position estimates based on alanine seem to also reflect microbial trophic steps. The difference between these estimations can provide an index of the microbial food web contribution to the trophic position of a particular consumer. Here, we examined the differences between both trophic position estimates through a meta‐analysis of the published data over a large range of marine consumers. The results suggest that the microbial contribution to trophic position decreases as trophic position increases in the marine pelagic food web, showing the lowest values in top consumers. Ureotelic organisms showed the highest microbial contributions. The current data point to an upper limit for the estimation of this index at δ15N values of glutamic acid or alanine of ~ 18.5‰, or trophic positions of 3.9 or 4.7 for fish and ammoniotelic organisms, respectively. Under the stable isotope lens, bacteria and protists may play a key role in the estimation of trophic positions of consumers. However, uncertainties that related both to the microbial loop (e.g., metabolic bacterial pathways) and to metazoan consumers (e.g., influence of the gut microbiome) still obscure the establishment of clear quantitative links between microbial and metazoan food webs.

Species' functional traits and interactions drive nitrate-mediated sulfur-oxidizing community structure and functioning.

Understanding the processes and mechanisms governing microbial community assembly and their linkages to ecosystem functioning has long been a core issue in microbial ecology. An in-depth insight still requires combining with analyses of species' functional traits and microbial interactions. Our study showed how species' functional traits and interactions determined microbial community structure and functions by a well-controlled laboratory experiment with nitrate-mediated sulfur oxidation systems using high-throughput sequencing and culture-dependent technologies. The results provided solid evidences that species' functional traits and interactions were the intrinsic factors determining community structure and function. More importantly, our study established quantitative links between community structure and function based on species' functional traits and interactions, which would have important implications for the design and synthesis of microbiomes with expected functions.

Hydrogen isotope biogeochemistry of plant waxes in paired lake catchments

Studies of plants in modern catchment systems can serve to improve the level of understanding of sedimentary plant wax hydrogen isotope (δ2H) data by directly investigating the link between local climate conditions, plant source water, leaf water, and plant lipids for individual plant species. Here we present such an application to compare two lake catchments with different basin morphologies in Estonia. We sampled leaf and xylem water, as well as leaf waxes from the seven common plant species in each catchment, and soil water. We then measured the δ2H values of all waters, and of n-alkanes (δ2Hn–alk) from the plant waxes, as well as surface lake sediments. We applied a Péclet modified Craig-Gordon leaf water model using local precipitation isotope and climate data to characterize the δ2H values of the biosynthetic source water pool throughout the entire growth season. The data and model results provide a detailed view of how the input hydro-climatic signals from the precipitation δ2H values were modified by environmental and plant physiological conditions and ultimately by the biosynthetic isotope fractionation associated with n–alkane production by each plant species. We report both average apparent (εapp = –92 ± 21 ‰) and biosynthetic (εbio = –132 ± 19 ‰) hydrogen isotope fractionation factors of all species from the studied catchments. This information serves as a foundation for sedimentary organic geochemistry and paleoclimate studies, which allows for more direct and quantitative links to be made between sedimentary plant wax δ2H values and the climate signal contained in plant source water.

Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex.

Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular, and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin-Huxley dynamics of >50,000 neurons belonging to 17 cell types. The model simultaneously captured spiking and CSD responses and demonstrated a two-way dissociation: firing rates are altered with minor effects on the CSD pattern by adjusting synaptic weights, and CSD is altered with minor effects on firing rates by adjusting synaptic placement on the dendrites. We describe how thalamocortical inputs and recurrent connections sculpt specific sinks and sources early in the visual response, whereas cortical feedback crucially alters them in later stages. These results establish quantitative links between macroscopic brain measurements (LFP/CSD) and microscopic biophysics-based understanding of neuron dynamics and show that CSD analysis provides powerful constraints for modeling beyond those from considering spikes.

Assessing the resilience of circularity in water management: a modeling framework to redesign and stress-test regional systems under uncertainty

ABSTRACT Contrary to the ‘make-use-dispose’ linearity of conventional management, circular economy design principles have been proposed as a resource management alternative that reduces waste and promotes efficiency. These principles also find use in water management, offering an alternative against centralized models. Despite the intrinsic links between circularity and resilience, few studies have advanced the identification and discussion of linkage beyond a theoretical or conceptual level. This study presents quantitative links between circularity and resilience, by demonstrating how different circular water management strategies lead to improved resilience performance for a regional urban-rural water system. A stress-testing framework based on a water cycle model is presented, where different circular interventions are evaluated in terms of their overall resilience against future uncertainty. The results demonstrate that circular water options lead to more resilient water systems. The more circular dimensions are addressed through interventions, the more robust resilience profiles become across different water cycle domains.

A meta-analysis suggests climate change shifts structure of regional communities of soil invertebrates

Soil animals perform a range of essential ecosystem functions and can modify the effects of global change on terrestrial ecosystems. We evaluated responses of six major groups of soil animals (Acari (all groups), Oribatida, Collembola, Insecta, Nematoda, and Oligochaeta) to controlled changes in air temperature, precipitation level or carbon dioxide concentration by using random-effects modelling and mixed-effects meta-regression modelling. Along with the three global change factors, sixteen local climatic characteristics (such as mean annual temperature, Köppen climate classification, vegetation type) were tested. Overall, 86 studies comprising 236 observations with mean duration of 51 months were selected as relevant for the analysis. Quantitative links between global change factors, local climate characteristics and changes in abundance of four taxonomic groups of soil animals were revealed. Warming and precipitation level were associated most strongly with population dynamics of soil invertebrates compared to elevated atmospheric CO2. Each 1 °C increase in air temperature was correlated with a mean of 12.5% (95% CI: 2.5%–22.6%) increase in Acari abundance, while populations of Collembola were declined by 9.6% (95% CI: −17.8% to −1.4%). Meanwhile, each 10% increase in precipitation level was correlated with the increase in the abundance of Nematoda by 1.4% (95% CI: −7.6% to 10.4%) and Oligochaeta by 34.7% (95% CI: 8.1%–61.2%). Considering IPCC estimates (SSP3-7.0 Scenario) of an average climate warming by 3.6 °C and a substantial variation in local precipitation levels (up to ±20%) by the end of the 21st century, strong local changes in the structure of detrital food webs are predicted by meta-regression models. In regions with decreased precipitation, the formation of soil food webs promoting carbon mineralization may be expected, while in regions with increased precipitation, the changes in detrital food web structure can contribute to the accumulation of carbon in the soil.

Flexoelectricity at fractal rough surfaces

This study quantifies the role of surface roughness on interfacial flexoelectricity under normal compression and oscillation by examining a series of 3D-printed surfaces with diverse roughness features. For incipient contact, the measured flexoelectric charge is found to follow a power-law dependence on normal compression load, with the exponent positively correlated with the fractal dimension. The value of this power-law exponent is similar to that for contact stiffness. The underlying mechanism for this coincidence is elucidated by contact analyses based on geometric truncations of surface structures. Contact micromechanics show that the interfacial flexoelectric charge will increasingly concentrate on large microcontacts as the compression continues. A rougher surface with higher fractal dimension tends to demonstrate less heterogeneity for flexoelectric polarizations over microcontacts. This study provides systematic experimental measurements and comprehensive explanations for interfacial flexoelectricity and establishes quantitative links to multi-scale surface structures, shedding light on novel approaches for contact evaluation and flexoelectricity enhancement.

A data driven approach to mineral chemistry unveils magmatic processes associated with long-lasting, low-intensity volcanic activity

The most frequent volcanic eruptions are of low-intensity and small magnitude. They produce abundant ash-sized (< 2 mm) clasts, which are too small to establish quantitative links between magmatic processes and eruptive dynamics using classic approaches. This inhibits our ability to study the past behaviour of frequently erupting volcanoes, essential to predict their future activity and mitigate their impact. The Palizzi unit (10–13th century, Vulcano, Italy) includes a prototype sequence of ash deposits resulting from prolonged Vulcanian eruptions punctuated by those of two larger sub-Plinian events. We apply Hierarchical Clustering to chemical analyses of clinopyroxene collected along the stratigraphy to decipher magma dynamics during this eruptive period. We identify periods of magma input and we link deep magmatic processes to eruptive dynamics, also showing that our approach can be used to connect magma and eruptive dynamics in any volcanic sequence. This is essential to track the processes occurring during frequent eruptions and to identify the build-up to larger explosive events.

Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin

Quantifying the effects of streamflow alteration on assemblage response is central to understanding the role humans play in shaping aquatic environments. These changes represent a level of complexity that impedes developing quantitative links between flow and ecological response because stream hydrology is strongly intertwined with natural and anthropogenic factors. Better management outcomes require disentangling these linkages. Benthic macroinvertebrate data were combined with GIS-derived natural and anthropogenic basin characteristics to identify factors associated with changes in flow and assemblage characteristics. Models linking streamflow metrics and macroinvertebrate response at basin- and subregion-scales were developed using boosted regression tree (BRT) analysis. Basin-scale BRT analyses revealed that links between macroinvertebrate response and streamflow metrics were often obscured, whereas more homogeneous subregions were better able to discern relations with flow. Urban land cover was the primary factor accounting for changes in flow characteristics. Elevation, land cover and high flow frequency were the principal variables driving changes in assemblage structure within subregions. Assemblage metrics and traits were equally useful for building response models and were affected similarly by flow alteration. Our results indicate that response models based on upland and lowland subregions performed better than basin-scale models. However, when defining subregions, care should be taken to maintain data sufficiency. Developing practical flow-protection standards that support a balance between human water requirements and ecological integrity requires models that reduce uncertainty and identify management-relevant drivers. However, effective management often differs by location and models developed at the subregion level may be more applicable to management and stakeholder interests.

Системный анализ как инструмент реализации малоотходного и безотходного производства на предприятиях нефтегазовой отрасли

The introduction of low–waste and waste–free technologies is an actual development direction for oil and gas enterprises, in particular for oil refineries. Development of methodological approaches to the analysis and synthesis of complex energy systems in order to increase the efficiency and environmental safety of the objects under study through the introduction of low–waste and waste–free technologies, the implementation of technological cycles close to closed. The solution of the task is carried out in accordance with the basic principles of the system analysis of complex objects, in stages: analysis of the structure of the object using the decomposition–aggregation approach; determination of internal and external links between the elements of the system; determination of the regularities of the functioning of the object and the complex of influencing factors. A block–hierarchical structure of an oil refining enterprise has been developed, which establishes quantitative and qualitative links between the elements of the system at all levels of the hierarchy. A system of performance indicators is proposed, in accordance with which an assessment is made of the current state of the object under study. The most energy–intensive production facilities of an oil refining enterprise have been determined, their exergy balances, have been calculated. The analysis made it possible to assess the potential for increasing environmental and energy efficiency, the main directions for the implementation of low–waste and waste–free technologies. Science–based directions for improving environmental safety and energy efficiency of the object of study are determined. Several options for schemes are proposed, an assessment of the energy and technical and economic efficiency of the integration of the proposed solutions into the energy complex of oil refineries is carried out. The results obtained confirm the feasibility of integrating the proposed scheme in an existing enterprise.

Sulfide intrusion in a habitat forming seagrass can be predicted from relative abundance of sulfur cycling genes in sediments

Sulfide intrusion from sediments is an increasingly recognized contributor to seagrass declines globally, yet the relationship between sediment microorganisms and sulfide intrusion has received little attention. Here, we use metagenomic sequencing and stable isotope (34S) analysis to examine this relationship in Cockburn Sound, Australia, a seagrass-dominated embayment with a gradient of sulfide stress and seagrass declines. There was a significant positive relationship between sulfide intrusion into seagrasses and sulfate reduction genes in sediment microbial communities, which was greatest at sites with long term seagrass declines. This is the first demonstration of a significant link between sulfur cycling genes present in seagrass sediments and sulfide intrusion in a habitat-forming seagrass that is experiencing long-term shoot density decline. Given that microorganisms respond rapidly to environmental change, the quantitative links established in this study can be used as a potential management tool to enable the prediction of sulfide stress on large habitat forming seagrasses; a global issue expected to worsen with climate change.

Geometric framework to predict structure from function in neural networks.

Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of threshold-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate the solution space of all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. A generalization accounting for noise further reveals that the solution space geometry can undergo topological transitions as the allowed error increases, which could provide insight into both neuroscience and machine learning. We ultimately use this geometric characterization to derive certainty conditions guaranteeing a nonzero synapse between neurons. Our theoretical framework could thus be applied to neural activity data to make rigorous anatomical predictions that follow generally from the model architecture.

Slave Past, Modern Lives: An Analysis of the Legacy of Slavery and Contemporary Life Expectancy in the American South

As questions about racial reparations have entered public and political discourse again, research about the long-term impact of chattel slavery—so called “legacy of slavery” research—has taken on new significance. Over the past two decades researchers have identified direct quantitative links between slavery and a number of contemporary social and economic outcomes, including income, poverty, home ownership, school segregation, crime, educational inequality, and political polarization. Recently, however, researchers have begun to connect slavery to contemporary health outcomes, showing the legacy of slavery seems to stunt the health of black Americans while bolstering the health of white Americans. This manuscript builds on that recent research by examining the connection between subnational variation in the density of slavery and life expectancy in the American South. Using a variety of data sources, such as the US Census, American Community Survey (ACS), the United States Department of Agriculture Economic Research Service, and the Robert Wood Johnson Foundation County Health Rankings, and spatially robust OLS regression analysis, I find that in southern counties where slavery was denser black life expectancy remains proportionally lower and white life expectancy remains proportionally higher than in southern counties where slavery was less dense.

Mechanism and performance relevance of nanomorphogenesis in polyamide films revealed by quantitative 3D imaging and machine learning.

Biological morphogenesis has inspired many efficient strategies to diversify material structure and functionality using a fixed set of components. However, implementation of morphogenesis concepts to design soft nanomaterials is underexplored. Here, we study nanomorphogenesis in the form of the three-dimensional (3D) crumpling of polyamide membranes used for commercial molecular separation, through an unprecedented integration of electron tomography, reaction-diffusion theory, machine learning (ML), and liquid-phase atomic force microscopy. 3D tomograms show that the spatial arrangement of crumples scales with monomer concentrations in a form quantitatively consistent with a Turing instability. Membrane microenvironments quantified from the nanomorphologies of crumples are combined with the Spiegler-Kedem model to accurately predict methanol permeance. ML classifies vastly heterogeneous crumples into just four morphology groups, exhibiting distinct mechanical properties. Our work forges quantitative links between synthesis and performance in polymer thin films, which can be applicable to diverse soft nanomaterials.