Scale Resolution Research Articles

TWISP: a transgenic worm for interrogating signal propagation in Caenorhabditis elegans.

Genetically encoded optical indicators and actuators of neural activity allow for all-optical investigations of signaling in the nervous system. But commonly used indicators, actuators, and expression strategies are poorly suited for systematic measurements of signal propagation at brain scale and cellular resolution. Large-scale measurements of the brain require indicators and actuators with compatible excitation spectra to avoid optical crosstalk. They must be highly expressed in every neuron but at the same time avoid lethality and permit the animal to reach adulthood. Their expression must also be compatible with additional fluorescent labels to locate and identify neurons, such as those in the NeuroPAL cell identification system. We present TWISP, a transgenic worm for interrogating signal propagation, that addresses these needs and enables optical measurements of evoked calcium activity at brain scale and cellular resolution in the nervous system of the nematode Caenorhabditis elegans. In every neuron we express a nonconventional optical actuator, the gustatory receptor homolog GUR-3 + PRDX-2, under the control of a drug-inducible system QF + hGR, and a calcium indicator GCAMP6s, in a background with additional fluorophores from the NeuroPAL cell ID system. We show that this combination, but not others tested, avoids optical crosstalk, creates strong expression in the adult, and generates stable transgenic lines for systematic measurements of signal propagation in the worm brain.

Distinguishing Elements at the Sub-Nanometer Scale on the Surface of a High Entropy Alloy.

Materials in crystalline form possess translational symmetry (TS) when the unit cell is repeated in real space with long- and short-range orders. The periodic potential in the crystal regulates the electron wave function and results in unique band structures, which further define the physical properties of the materials. Amorphous materials lack TS due to the randomization of distances and arrangements between atoms, causing the electron wave function to lack a well-defined momentum. High entropy materials provide another way to break the TS by randomizing the potential strength at periodic atomic sites. The local elemental distribution has a great impact on physical properties in high entropy materials. It is critical to distinguish elements at the sub-nanometer scale to uncover the correlations between the elemental distribution and the material properties. Here, the use of synchrotron X-ray scanning tunneling microscopy (SX-STM) with sub-nm scale resolution in identifying elements on a high entropy alloy (HEA) surface is demonstrated. By examining the elementally sensitive X-ray absorption spectra with an STM tip to enhance the spatial resolution, the elemental distribution on an HEA's surface at a sub-nm scale is extracted. These results open a pathway towardsquantitativelyunderstanding high entropy materials and their material properties.

The recent star formation histories of nearby galaxies on resolved scales

ABSTRACT Star formation histories (SFHs) of galaxies are affected by a variety of factors, both external (field versus cluster/group) and internal [presence of a bar and active galactic nucleus (AGN), morphological type]. In this work, we extend our previous study and apply the $\langle \mathrm{SFR}_{\textnormal {5}} \rangle \big / \langle \mathrm{SFR}_{\textnormal {200}} \rangle$ metric to a sample of 11 nearby galaxies with Multi-Unit Spectroscopic Explorer observations. Based on a combination of H α and ultraviolet photometry, $\langle \mathrm{SFR}_{\textnormal {5}} \rangle \big / \langle \mathrm{SFR}_{\textnormal {200}} \rangle$ is sensitive to star formation time-scales of ∼5–200 Myr and therefore measures the present-day rate of change in the star formation rate (SFR), dSFR/dt. Within this limited galaxy sample, we do not observe systematic variations between the global value of $\langle \mathrm{SFR}_{\textnormal {5}} \rangle \big / \langle \mathrm{SFR}_{\textnormal {200}} \rangle$ and the presence of an AGN, stellar bar, or group or cluster membership. Within some of the individual galaxies, we, however, observe significant differences in $\langle \mathrm{SFR}_{\textnormal {5}} \rangle \big / \langle \mathrm{SFR}_{\textnormal {200}} \rangle$ between the arm and interarm regions. In half of the galaxies, the recent SFH of both arm and interarm regions has been very similar. However, in the galaxies with higher bulge-to-total light ratios and earlier morphological type, the SFR is declining more rapidly in the interarm regions. This decline in SFR is not a result of low molecular gas surface density or a decrease in the star formation efficiency, implying that other factors are responsible for this SFR decrease.

Detection of cucumber downy mildew spores based on improved YOLOv5s

Cucumber downy mildew is caused by the infection of leaves with downy mildew spores. However, research on the prevention and control of cucumber downy mildew often focuses on the stage after symptoms have appeared on the leaves, that is, once disease spots have already formed. Since the occurrence of downy mildew is closely related to the quantity of spores, early-stage research on the quantity of downy mildew spores is of great significance for the prevention and control of cucumber downy mildew. Consequently, developing a rapid, accurate, and efficient method for detecting cucumber downy mildew spores is critical for advancing disease control. This study introduces an improved YOLOv5s model for spore detection. The model incorporates a transformer module into YOLOv5s’s backbone, enhancing global feature information extraction. It also adds a small object detection head to counter YOLOv5s’s extensive down-sampling and difficulty in learning features of small objects. Integration with the Convolutional Block Attention Module (CBAM) further refines detection precision for small objects like mildew spores. Upon evaluation with an image dataset collected through a microscope, the improved YOLOv5s model demonstrated superior performance metrics across various resolutions. At a resolution of 1440px × 1440px, it achieved the highest mean Average Precision (mAP@.5) of 95.4 %, a precision (P) score of 89.1 %, and a recall (R) rate of 90.3 %. These metrics surpassed the original YOLOv5s model at the same 1440px × 1440px resolution by 1.6 % in mAP@.5, 1.6 % in P, and 0.5 % in R. Additionally, the model’s mAP@.5 across various resolution scales indicates superior detection precision compared to other leading models like YOLOv7. In the context of microscopic images with small spores and complex backgrounds, the improved YOLOv5s model effectively detects cucumber downy mildew spores, offering valuable insights and technical support for advancing the prevention and control measures against cucumber downy mildew.

Spatial and Temporal Changes in Nutrient Source Contribution in a Lowland Catchment Within the Baltic Sea Region Under Climate Change Scenarios

AbstractCurrently, climate change is considered as an important factor affecting nutrient loads introduced through riverine systems into the Baltic Sea. Although the prospect of a large increase in pollution has long seemed very real, it still does not translate into planning of effective remedial actions. One of the factors limiting the development of such activities is the scale of simulations, focusing generally on catchment outlet profiles. To fill this gap and enable a step forward in understanding responses toward future predictions in a higher resolution scale (subcatchment), we assessed nutrient load contribution using calculation profiles localized along a main watercourse and its tributaries. To track spatial and seasonal changes of total nitrogen and phosphorus for the Wełna River (central Poland), we used climate change data and the SWAT model. Having at our disposal a catchment model with a good performance we could follow not only total load changes in particular subcatchments, but also track localization of the pollution sources and their direct impact on load estimations. Our results showed an increase of the loads, especially from the agricultural land use type, up to 34% for TN and 85% for TP, in the most extreme scenario. Moreover, forest areas have been noted as highly reactive to climate change, and through their localisation are able to distinctly alter nutrient outflow. Finally, the contribution of urban areas should be further investigated since the dynamics of nitrogen and phosphorus release from impervious surfaces is noticeably different here than from the other diffuse sources.

Early Blight and Late Blight Disease Detection in Potato Using Efficientnetb0

Potatoes are an important crop heavily consumed by Indian food products. It is produced on a massive scale, with China, India, Russia, Poland, and the USA being the main producers. Numerous leaf diseases harm the crop during its production. A typical Indian farmer lacks the tools necessary to detect Leaf Disease before damage is done. On a dataset of potato leaf images retrieved from Kaggle, we employed the EfficientNetB0 of Deep Learning to address this problem. This model uses width scaling and resolution scaling apart from depth scaling to perform the classification. Our work mainly focuses on the diseases Early Blight and Late Blight, two serious potato diseases. Early blight Spots start off as tiny, dry, dark, and papery specks that develop into brown to black, circular to oval-shaped regions. Veins that round the spots frequently give them an angular appearance. Late blight syntoms appear as small, light to dark green and round to irregularly shaped. Water-soaked patches are the first signs of late blight. The Data Collection has 2152 pictures in total, 2000 of which are diseased and 152 of which are healthy. The deep learning model provides a testing accuracy of 99.05%, which is higher than several widely used techniques available to provide farmers with knowledge about correct diseases well in time.

Incorporating Intracellular Processes in Virus Dynamics Models.

In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus-host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models.

Climate walking and linear mixed model statistics for the seasonal outdoor thermophysiological comfort assessment in Lisbon

To measure urban outdoor pedestrian thermophysiological comfort mobile roving missions were conducted in all seasons of the year during the day and at night. Six routes were chosen, representing areas of the city with varying urban morphological layouts. The thermal comfort conditions were analysed using the Local Climate Zone spatial scale. This enabled the identification of typologies of areas where pedestrians might feel (un)comfortable. To achieve the proposed objectives, quantitative and qualitative analysis were run, namely a Linear Mixed-Effect Model. This model was useful to understand the thermal behaviour of the roved areas by comparison to a reference area. The city was found to be generally cooler, except at night when it can have a 44% increased UTCI. Most collected values throughout the year had ‘no thermal stress’, except in the summer when ‘moderate’ and ‘strong heat stress’ was predominant. Compact and large low rise urban areas were found to be the most uncomfortable. Significant amplitudes were found in some LCZs, reaching as high as 7 °C in some scenarios. Sparsely wooded areas exhibited higher values in all seasons and at night, except in the summer. This showed that for a local heat stress analysis a higher resolution scale is needed.

Multiscale X-ray phase-contrast CT unveils the evolution of bile infarct in obstructive biliary disease

Bile infarct is a pivotal characteristic of obstructive biliary disease, but its evolution during the disease progression remains unclear. Our objective, therefore, is to explore morphological alterations of the bile infarct in the disease course by means of multiscale X-ray phase-contrast CT. Bile duct ligation is performed in mice to mimic the obstructive biliary disease. Intact liver lobes of the mice are scanned by phase-contrast CT at various resolution scales. Phase-contrast CT clearly presents three-dimensional (3D) images of the bile infarcts down to the submicron level with good correlation with histological images. The CT data illustrates that the infarct first appears on day 1 post-BDL, while a microchannel between the infarct and hepatic sinusoids is identified, the number of which increases with the disease progression. A 3D model of hepatic acinus is proposed, in which the infarct starts around the portal veins (zone I) and gradually progresses towards the central veins (zone III) during the disease process. Multiscale phase-contrast CT offers the comprehensive analysis of the evolutionary features of the bile infarct in obstructive biliary disease. During the course of the disease, the bile infarcts develop infarct-sinusoidal microchannels and gradually occupy the whole liver, promoting the disease progression.

Lower secondary school students’ conversations when constructing representations of plate boundaries in Minecraft

This article explores students’ (13 y) conversations when they construct representations of plate boundaries in the digital gaming environment of Minecraft Education. It also investigates how the spatial affordance of Minecraft Education may support students’ meaning making of the theory of plate tectonics. Video data of classroom activities with Minecraft Education was collected with head cameras. Analysis of the data shows that students overall used everyday language during the activity of constructing plate boundaries. The study suggests i.) a need for more teacher-centeredness and structured scaffolding of scientific language during small group work in Minecraft, and ii.) a need to increase the spatial extent in Minecraft Education to include both the subsurface and more regional scales of resolution.

Improving the characterization of red coloring matter from prehistoric cave art by means of laboratory confocal XRF depth profiling combined with synchrotron XRF imaging

Chemical in situ study of red coloring matter in cave art is challenging because characteristic trace elements can be present both in the matter and in the wall support, and the latter has a quite heterogeneous composition.In this study, a stalactite presenting red iron oxide coloring matter from La Garma cave, Northern Spain, has been analyzed with complementary techniques. The aims are, on the one hand, confirming for the first time the feasibility of confocal XRF (CXRF) depth-resolved scans to successfully separate the complex stratigraphy of red prehistoric coloring matter on a calcitic support. On the other hand, finding differentiation criteria and improving the characterization of this red iron-based coloring matter would help understand and virtually separate it from its support, as well as improve future in situ analyses in cave sites.CXRF depth-resolved scans were performed using the LouX3D device available in our laboratory. With this technique, we can precisely determine the chemical depth composition of an object, layer by layer, with a spatial resolution in the micrometer scale. These measurements were combined with a synchrotron induced micro-X-ray fluorescence (SR-µXRF) chemical mapping performed at the PUMA beamline, SOLEIL synchrotron. These highly sensitive measurements not available with any other non-destructive lab-based technique allow seeing a greater number of trace and minor elements associated with Fe. Complementing them with the direct depth-resolved information given by CXRF, it was possible to find differentiation criteria distinguishing the wall support and the coloring matter, finely characterize this red iron-based matter, and confirm the feasibility as well as the advantages of applying CXRF for the non-invasive technical study of prehistoric cave figures.

Evidence of scale-free clusters of vegetation in tropical rainforests.

Tropical rainforests exhibit a rich repertoire of spatial patterns emerging from the intricate relationship between the microscopic interaction between species. In particular, the distribution of vegetation clusters can shed much light on the underlying process that regulates the ecosystem. Analyzing the distribution of vegetation clusters at different resolution scales, we show the first robust evidence of scale-invariant clusters of vegetation, suggesting the coexistence of multiple intertwined scales in the collective dynamics of tropical rainforests. We use field data and computational simulations to confirm our hypothesis, proposing a predictor that could be particularly interesting to monitor the ecological resilience of the world's "green lungs."

Fine-Scale Spatial Variability of Greenhouse Gas Emissions From a Subantarctic Peatland Bog.

Peatlands are recognized as crucial greenhouse gas sources and sinks and have been extensively studied. Their emissions exhibit high spatial heterogeneity when measured on site using flux chambers. However, the mechanism by which this spatial variability behaves on a very fine scale remains unclear. This study investigates the fine-scale spatial variability of greenhouse gas emissions from a subantarctic Sphagnum peatland bog. Using a recently developed skirt chamber, methane emissions and ecosystem respiration (as carbon dioxide) were measured at a submeter scale resolution, at five specific 3 × 3 m plots, which were examined across the site throughout a single campaign during the Austral summer season. The results indicated that methane fluxes were significantly less homogeneously distributed compared with ecosystem respiration. Furthermore, we established that the spatial variation scale, i.e., the minimum spatial domain over which notable changes in methane emissions and ecosystem respiration occur, was <0.56 m2. Factors such as ground height relative to the water table and vegetation coverage were analyzed. It was observed that Tetroncium magellanicum exhibited a notable correlation with higher methane fluxes, likely because of the aerenchymatous nature of this species, facilitating gas transport. This study advances understanding of gas exchange patterns in peatlands but also emphasizes the need for further efforts for characterizing spatial dynamics at a very fine scale for precise greenhouse gas budget assessment.

Who Should We Blame for Android App Crashes? An In-Depth Study at Scale and Practical Resolutions

Android system has been widely deployed in energy-constrained IoT devices for many practical applications, such as smart phone, smart home, healthcare, fitness, and beacons. However, Android users oftentimes suffer from app crashes, which directly disrupt user experience and could lead to data loss. Till now, the community have limited understanding of their prevalence, characteristics, and root causes. In this article, we make an in-depth study of the crash events regarding ten very popular apps of different genres, based on fine-grained system-level traces crowd-sourced from 93 million Android devices. We find that app crashes occur prevalently on the various hardware models studied, and better hardware does not seem to essentially relieve the problem. Most importantly, we unravel multi-fold root causes of app crashes, and pinpoint that the most crashes stem from the subtle yet crucial inconsistency between app developers’ supposed memory/process management model and Android’s actual implementations. We design practical approaches to addressing the inconsistency; after large-scale deployment, they reduce 40.4% of the app crashes with negligible system overhead. In addition, we summarize important lessons learned from this study, and have released our measurement code/data to the community.

Energy-conserving neural network for turbulence closure modeling

In turbulence modeling, we are concerned with finding closure models that represent the effect of the subgrid scales on the resolved scales. Recent approaches gravitate towards machine learning techniques to construct such models. However, the stability of machine-learned closure models and their abidance by physical structure (e.g. symmetries, conservation laws) are still open problems. To tackle both issues, we take the ‘discretize first, filter next’ approach. In this approach we apply a spatial averaging filter to existing fine-grid discretizations. The main novelty is that we introduce an additional set of equations which dynamically model the energy of the subgrid scales. Having an estimate of the energy of the subgrid scales, we can use the concept of energy conservation to derive stability. The subgrid energy containing variables is determined via a data-driven technique. The closure model is used to model the interaction between the filtered quantities and the subgrid energy. Therefore the total energy should be conserved. Abiding by this conservation law yields guaranteed stability of the system. In this work, we propose a novel skew-symmetric convolutional neural network architecture that satisfies this law. The result is that stability is guaranteed, independent of the weights and biases of the network. Importantly, as our framework allows for energy exchange between resolved and subgrid scales it can model backscatter. To model dissipative systems (e.g. viscous flows), the framework is extended with a diffusive component. The introduced neural network architecture is constructed such that it also satisfies momentum conservation. We apply the new methodology to both the viscous Burgers' equation and the Korteweg-De Vries equation in 1D. The novel architecture displays superior stability properties when compared to a vanilla convolutional neural network.

Turbulence induced by a swarm of rising bubbles from coarse-grained simulations

We performed numerical simulations of a homogeneous swarm of bubbles rising at large Reynolds number, $Re=760$ , with volume fractions ranging from 1 % to 10 %. We consider a simplified model in which the interfaces are not resolved, but which allows us to simulate flows with a large number of bubbles and to emphasize the interactions between bubble wakes. The liquid phase is described by solving, on an Eulerian grid, the Navier–Stokes equations, including sources of momentum which model the effect of the bubbles. The dynamics of each bubble is determined within the Lagrangian framework by solving an equation of motion involving the hydrodynamic forces exerted by the fluid accounting for the correction of the fictitious self-interaction of a bubble with its own wake. The comparison with experiments shows that this coarse-grained simulations approach can reliably describe the dynamics of the resolved flow scales. We use conditional averaging to characterize the mean bubble wakes and obtain in particular the typical shear imposed by the rising bubbles. On the basis of the spectral decomposition of the energy budget, we observe that the flow is dominated by production at large scales and by dissipation at small scales and we rule out the presence of an intermediate range in which the production and dissipation are locally in balance. We propose that the $k^{-3}$ subrange of the energy spectra results from the mean shear rate imposed by the bubbles, which controls the rate of return to isotropy.

The mediating role of mothers’ conflict resolution skills in the relationship between mothers’ communication skills and behavioral problems of children aged 3–6 years

Although studies have revealed the importance and effect of mothers' communication levels on behavioral problems seen in children, the role of the other key variable in this relationship has not been adequately examined. This study aims to test a theoretical model developed based on the relevant literature to evaluate the mediating role of the mother's conflict resolution skills in the relationship between the behavioral problems of children aged 3–6 years and the mother's communication skills. The study was prepared based on the correlational survey model. Its sample consists of 410 mothers who have children in the age group of 3–6 years and live within the borders of Northern Cyprus. The mothers included in the sample were contacted using a stratified sampling method. The personal information form, Communication Skills Scale-Adult Form, Conflict Resolution Scale, and Social Competence and Behavior Assessment Scale were used to collect the data. Statistical data were analyzed using the SPSS 24.0 and Amos 21.0 software. The statistical analysis of the research data indicates that the fit indices, except for RMSEA, demonstrate that the χ2/df value has an acceptable fit and the GFI, NFI, and CFI values fall within the fit limits. According to the research findings, mothers' tendencies in conflict resolution significantly predict children's social competence and behaviors. Furthermore, the study reveals that mothers' communication skills do not significantly predict children's social competence and behaviors. As a result, it is determined that mothers' conflict resolution tendencies mediate the relationship between their communication skills and children's social competence and behaviors. In light of these findings, several recommendations are proposed for future research and practical applications.

Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction (LSP). To illustrate various study area scales, Ganzhou City in China, its eastern region (Ganzhou East), and Ruijin County in Ganzhou East were chosen. Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m, as well as slope units that were extracted by multi-scale segmentation method. The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs. Then, landslide susceptibility maps (LSMs) of Ganzhou City, Ganzhou East and Ruijin County are produced using a support vector machine (SVM) and random forest (RF), respectively. The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City, along with the LSMs of Ruijin County from Ganzhou East. Additionally, LSMs of Ruijin at various mapping unit scales are generated in accordance. Accuracy and landslide susceptibility indexes (LSIs) distribution are used to express LSP uncertainties. The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City, Ganzhou East to Ruijin County, whereas those under slope units are less affected by study area scales. Of course, attentions should also be paid to the broader representativeness of large study areas. The LSP accuracy of slope units increases by about 6%–10% compared with those under grid units with 30 m and 60 m resolution in the same study area's scale. The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large. The importance of environmental factors varies greatly with the 60 m grid unit, but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.Graphic abstract

Exploring evolutionary trajectories in ovarian cancer patients by longitudinal analysis of ctDNA.

We analysed whether temporal heterogeneity of ctDNA encodes evolutionary patterns in ovarian cancer. Targeted sequencing of 275 cancer-associated genes was performed in a primary tumor biopsy and in ctDNA of six longitudinal plasma samples from 15 patients, using the Illumina platform. While there was low overall concordance between the mutational spectrum of the primary tumor biopsies vs. ctDNA, TP53 variants were the most commonly shared somatic alterations. Up to three variant clusters were detected in each tumor biopsy, likely representing predominant clones of the primary tumor, most of them harbouring a TP53 variant. By tracing these clusters in ctDNA, we propose that liquid biopsy may allow to assess the contribution of ancestral clones of the tumor to relapsed abdominal masses, revealing two evolutionary patterns. In pattern#1, clusters detected in the primary tumor biopsy were likely relapse seeding clones, as they contributed a major share to ctDNA at relapse. In pattern#2, similar clusters were present in tumors and ctDNA; however, they were entirely cleared from liquid biopsy after chemotherapy and were undetectable at relapse. ctDNA private variants were present among both patterns, with some of them mirroring subclonal expansions after chemotherapy. We demonstrate that tracing the temporal heterogeneity of ctDNA, even below exome scale resolution, deciphers evolutionary trajectories in ovarian cancer. Furthermore, we describe two evolutionary patterns that may help to identify relapse seeding clones for targeted therapy.

Seeing is believing: Correlating optoelectronic functionality with atomic scale imaging of single semiconductor nanocrystals.

A unique on-chip method for the direct correlation of optical properties, with atomic-scale chemical-structural characteristics for a single quantum dot (QD), is developed and utilized in various examples. This is based on performing single QD optical characterization on a modified glass substrate, followed by the extraction of the relevant region of interest by focused-ion-beam-scanning electron microscope processing into a lamella for high resolution scanning transmission electron microscopy (STEM) characterization with atomic scale resolution. The direct correlation of the optical response under an electric field with STEM analysis of the same particle allows addressing several single particle phenomena: first, the direct correlation of single QD photoluminescence (PL) polarization and its response to the external field with the QD crystal lattice alignment, so far inferred indirectly; second, the identification of unique yet rare few-QD assemblies, correlated directly with their special spectroscopic optical characteristics, serving as a guide for future designed assemblies; and third, the study on the effect of metal island growth on the PL behavior of hybrid semiconductor-metal nanoparticles, with relevance for their possible functionality in photocatalysis. This work, therefore, establishes the use of the direct on-chip optical-structural correlation method for numerous scenarios and timely questions in the field of QD research.