Level Of Resolution Research Articles

Intelligent diagnosis of flip chip solder joints with resolution enhanced SAM image

Scanning acoustic microscopy (SAM) technique has been applied to defect inspection in electronic devices. With the increase of packaging density, detection of the micro-defects in high density devices becomes more and more challenging. The SAM test is suffering from sacrificing the spatial resolution to reach a certain penetration depth of the ultrasonic waves. So it is necessary to enhance the resolution level of the SAM image. In this paper, a wavelet based resolution enhancement technique was investigated to reconstruct a high quality image for SAM test of the flip chip packages. The stationary wavelet transform was adopted to decompose the captured SAM image into four frequency subbands, and the high frequency subbands were enhanced by adding the difference matrix in the intermediate stage, and a super resolution SAM image was derived from combining all the subbands by using the Inverse Discrete Wavelet Transform. Then the solder joints segmented from the SR-SAM image were classified by using the SVM algorithm. The results validated that the proposed technique is effective to improve the detection accuracy of SAM test.

Bridging the Divide from Paleoecology to Neoecology

As paleoecologists we often claim that our science is relevant to conservation, but relatively few management plans are steered by paleoecological insights. One of our common justifications that paleoecology should feature in conservation is to determine what is ‘natural’. Intergenerational perceptions of naturalness are shaped by our experiences of ecosystems continuously and progressively influenced by human-induced stresses – the concept of shifting baselines. Those stresses have been present since the last ice-age in the tropical Andes, when fire regimes and megafaunal extinctions pre-dated the onset of the Holocene. Whereas human-induced ecological change is only evident for 200 years on the Galapagos Islands. But both instances are consistent that they show that low numbers of people, probably not even living in permanent settlements wrought lasting ecological changes and extinctions. Cascading effects of tortoise loss were seen on Galapagos vegetation that directly or indirectly led to extinctions among endemic plants. Making paleoecology relevant to conservation requires finding levels of taxonomic and temporal resolution that are relevant to land managers and providing concrete recommendations for restoration.

High depth resolution wavelength scanning interferometry with narrow scanning bandwidth via sinewaves separation

Wavelength scanning interferometry (WSI) is a promising tomographic imaging technique. However, the depth resolution is fundamentally limited by a narrow wavelength scanning bandwidth, which brings challenges to the frequency extraction for depth information. In this work, we proposed a parameter estimation (PE) paradigm via sinewaves separation, which allows high depth resolution tomographic measurement with narrow-band sources. To obtain accurate depth information, a sinewaves separation model is designed to estimate the frequency of the overlapped signals and it can achieve higher depth resolution without any prior knowledge even under limited scanning bandwidth. Moreover, the estimated frequency is used for phase retrieval and the multi-layer 3D profile measurement can be further implemented. The simulation and experimental results show that the PE method can reach the same depth resolution level as discrete Fourier transform (DFT) even reducing 60% scanning bandwidth when PSNR is higher than 35 dB.

Regulon active landscape reveals cell development and functional state changes of human primary osteoblasts in vivo

BackgroundWhile transcription factor (TF) regulation is known to play an important role in osteoblast development, differentiation, and bone metabolism, the molecular features of TFs in human osteoblasts at the single-cell resolution level have not yet been characterized. Here, we identified modules (regulons) of co-regulated genes by applying single-cell regulatory network inference and clustering to the single-cell RNA sequencing profiles of human osteoblasts. We also performed cell-specific network (CSN) analysis, reconstructed regulon activity-based osteoblast development trajectories, and validated the functions of important regulons both in vivo and in vitro.ResultsWe identified four cell clusters: preosteoblast-S1, preosteoblast-S2, intermediate osteoblasts, and mature osteoblasts. CSN analysis results and regulon activity-based osteoblast development trajectories revealed cell development and functional state changes of osteoblasts. CREM and FOSL2 regulons were mainly active in preosteoblast-S1, FOXC2 regulons were mainly active in intermediate osteoblast, and RUNX2 and CREB3L1 regulons were most active in mature osteoblasts.ConclusionsThis is the first study to describe the unique features of human osteoblasts in vivo based on cellular regulon active landscapes. Functional state changes of CREM, FOSL2, FOXC2, RUNX2, and CREB3L1 regulons regarding immunity, cell proliferation, and differentiation identified the important cell stages or subtypes that may be predominantly affected by bone metabolism disorders. These findings may lead to a deeper understanding of the mechanisms underlying bone metabolism and associated diseases.

A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India

Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015–16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.

The Application of Single-Cell RNA Sequencing in the Inflammatory Tumor Microenvironment.

The initiation and progression of tumors are complex. The cancer evolution-development hypothesis holds that the dysregulation of immune balance is caused by the synergistic effect of immune genetic factors and environmental factors that stimulate and maintain non-resolving inflammation. Throughout the cancer development process, this inflammation creates a microenvironment for the evolution and development of cancer. Research on the inflammatory tumor microenvironment (TME) explains the initiation and progression of cancer and guides anti-cancer immunotherapy. Single-cell RNA sequencing (scRNA-seq) can detect the transcription levels of cells at the single-cell resolution level, reveal the heterogeneity and evolutionary trajectory of infiltrated immune cells and cancer cells, and provide insight into the composition and function of each cell group in the inflammatory TME. This paper summarizes the application of scRNA-seq in inflammatory TME.

UAV data collection parameters impact on accuracy of Scots pine stand mensuration

A wide range of UAV systems used for forest research requires unified approaches to data collection. The research aims to determine the optimal parameters for UAV data collection to obtain accurate information about stands, considering the cost of resources for its collection. The process of collecting remote sensing data consisted of nine combinations divided into three levels of overlap and three levels of spatial resolution (survey altitude) and changing the degree of filtering of a dense point cloud during image processing. Individual tree detectingin the stand was performed using the R programming language and the ForestTools package. The results of the assessment of the dependence of the radius of tree crowns on their height were used to set the parameters of the variable filter function for finding local maxima for Scots pine stands. Errors in the identification of treetops were estimated using the F-score. The identified heights were compared with the field data of the ground survey. The proportion of classified digital elevation model DEM in the dense point cloud was reduced from the total area of the test site using images of 4.1 cm/pix spatial resolution (150 m survey altitude). The study presents the results of assessing the impact of spatial resolution of optical images collected from UAVs and their overlap on the results of measurements of stands parameters. It is determined that a photogrammetric survey with input images with a longitudinal overlap of less than 90% is not appropriate for the study of forest areas due to the impossibility of aligning all images. The results of the assessment of tree accounting in the stand showed that it is most appropriate to use images with a spatial resolution of up to 3.3 cm/pix (120 m survey altitude), otherwise, the proportion of missed treetops increases. Reducing the spatial resolution of remote sensing data leads to an increase in errors in determining the height of individual trees, and the average heights of the experimental plots had the same trend. Given the combination of the assessed factors, it is not recommended to use images with a spatial resolution of more than 3.3 cm/pix for forestry research due to increased errors in the individual tree detection and tree height determination. The results obtained can be used to select data collection parameters for research on Scots pine stands to assess their growing stock and phytomass

French dispatch: GTM-based analysis of the Chimiothèque Nationale Chemical Space.

In order to analyze the Chimiothèque Nationale (CN) - The French National Compound Library - in the context of screening and biologically relevant compounds, the library was compared with ZINC in-stock collection and ChEMBL. This includes the study of chemical space coverage, physicochemical properties and Bemis-Murcko (BM) scaffold populations. More than 5 K CN-unique scaffolds (relative to ZINC and ChEMBL collections) were identified. Generative Topographic Maps (GTMs) accommodating those libraries were generated and used to compare the compound populations. Hierarchical GTM («zooming») was applied to generate an ensemble of maps at various resolution levels, from global overview to precise mapping of individual structures. The respective maps were added to the ChemSpace Atlas website. The analysis of synthetic accessibility in the context of combinatorial chemistry showed that only 29,7 % of CN compounds can be fully synthesized using commercially available building blocks.

A Closed-Loop Network for Single Infrared Remote Sensing Image Super-Resolution in Real World

Single image super-resolution (SISR) is to reconstruct a high-resolution (HR) image from a corresponding low-resolution (LR) input. It is an effective way to solve the problem that infrared remote sensing images are usually suffering low resolution due to hardware limitations. Most previous learning-based SISR methods just use synthetic HR-LR image pairs (obtained by bicubic kernels) to learn the mapping from LR images to HR images. However, the underlying degradation in the real world is often different from the synthetic method, i.e., the real LR images are obtained through a more complex degradation kernel, which leads to the adaptation problem and poor SR performance. To handle this problem, we propose a novel closed-loop framework that can not only make full use of the learning ability of the channel attention module but also introduce the information of real images as much as possible through a closed-loop structure. Our network includes two independent generative networks for down-sampling and super-resolution, respectively, and they are connected to each other to get more information from real images. We make a comprehensive analysis of the training data, resolution level and imaging spectrum to validate the performance of our network for infrared remote sensing image super-resolution. Experiments on real infrared remote sensing images show that our method achieves superior performance in various training strategies of supervised learning, weakly supervised learning and unsupervised learning. Especially, our peak signal-to-noise ratio (PSNR) is 0.9 dB better than the second-best unsupervised super-resolution model on PROBA-V dataset.

Kepuasan Pernikahan dan Conflict Resolution pada Pasangan Long Distance Marriage

Long Distance Marriage or also called long-distance marriage, is a familiar phenomenon, including in Indonesia. Long Distance Marriage describes a situation where couples are physically separated. One partner has to go elsewhere for specific purposes, such as work, or the other partner has to stay at home or in their area of ​​origin. Long-distance marriages also cause many wives to experience loneliness because their husbands have left them for months. Physical separation from people considered close is often a painful experience and can affect almost every aspect of life.This study aims to determine the relationship between marital satisfaction and conflict resolution in long-distance marriage couples. This study uses a quantitative correlational method. The data collection technique used the purposive sampling method, determining the sample with specific considerations. The number of participants in this study was 66 out of a total population of 260, with criteria including Married men/women, under 40 years of age and undergoing long-distance marriages.The results of this study indicate that there is a relationship between marital satisfaction and conflict resolution. This is indicated by the coefficient values ​​of the two variables, namely Rxy = 0.246 with FCount = 4.123 (FCount>3.986) and P = 0.046 (p <0.05). There is a positive relationship between marital satisfaction and conflict resolution. This is indicated by the value of the correlation coefficient on marital satisfaction and conflict resolution variables, which is 6.1%. This means that the higher the marital satisfaction, the higher the level of conflict resolution in long-distance marriage couples. Vice versa, the lower the marital satisfaction, the lower the level of conflict resolution in long-distance marriage couples.

Improving the predictive performance of CLUE-S by extending demand to land transitions: The trans-CLUE-S model

The CLUE-S model is a popular choice for modelling land use and land cover change from local to regional scales, but it spatially allocates the demand for only the total cover of each land class in the predicted map. In the present work, we introduce a CLUE-S variant that allocates demand at the more detailed level of land type transitions: the trans-CLUE-S model. We implemented this extension algorithmically in R, without the need of new parameters. By processing each row of the land transition matrix separately, the model allocates the demand of each land category's transitions via the CLUE-S allocation routine for only the cells which were of that category in the map of the previous time step. We found that the trans-CLUE-S model had half the total and configuration disagreement of the CLUE-S predictions in an empirical landscape, and in simulated landscapes of different characteristics. Moreover, the trans-CLUE-S performance was less sensitive to the number of environmental predictors of land type suitability for allocating demand. Although trans-CLUE-S is computationally more demanding due to running a CLUE-S allocation for each land class, we appended the solution of a land-use assignment optimisation problem that facilitates the convergence and acceleration of allocation. We additionally provide R functions for: CLUE-S variants at other levels of demand resolution; random instead of environment-based allocation; and for simulating landscapes of desired characteristics. Our R code for the models and functions can contribute to more reproducible, transparent and accurate modelling, analysis and interpretation of land cover change.

Comparing sexual orientation identity development in plurisexual versus monosexual latinx sexual minority youth.

Plurisexual individuals represent 50% of sexual minority adolescent communities. However, most studies of sexual minority adolescents analyze this population as a homogeneous group rather than attending to plausible differences between plurisexual and monosexual sexual minority youth. This study examined whether plurisexual Latinx youth differed from monosexual youth on three facets of sexual orientation identity (SOI) development (exploration, resolution, and affirmation). Further, we examined whether plurisexual (vs. monosexual) identity moderated the relationship between SOI development and depressive symptoms and self-esteem. The sample included 365 Latinx sexual minority youth (14-24 years) living in the United States or US Territory. The majority of participants identified as monosexual (84.1%); 15.9% were plurisexual. The sample was 75.6% male, 19.5% female, and 2.0% transgender. Participants were asked to complete an online, cross-sectional survey that asked questions pertaining to ethnicity and sexual orientation. This data was collected in the fall of 2014. Latinx plurisexual youth had higher levels of SOI affirmation, exploration, and resolution than monosexual youth. SOI affirmation and exploration and were positively associated with self-esteem for all youth. SOI affirmation was significantly negatively associated with depressive symptoms for monosexual youth but not for plurisexual youth. SOI exploration and resolution were not significantly associated with depressive symptoms. This study demonstrates that there are significant differences between plurisexual and monosexual Latinx adolescent populations in reference to identity affirmation, exploration, and resolution, and that these sexual orientations should be treated distinctly in future research. In addition, all three aspects of identity development were significantly related to higher self-esteem for all youth regardless of identity, providing evidence for the importance of developing these three factors.

Fast, Accurate, and System-Specific Variable-Resolution Modeling of Proteins.

In recent years, a few multiple-resolution modeling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, with the remainder of the system being concurrently treated using a coarse-grained model. In most cases, the parametrization of the latter requires lengthy reference all-atom simulations and/or the usage of off-shelf coarse-grained force fields, whose interactions have to be refined to fit the specific system under examination. Here, we overcome these limitations through a novel multiresolution modeling scheme for proteins, dubbed coarse-grained anisotropic network model for variable resolution simulations, or CANVAS. This scheme enables a user-defined modulation of the resolution level throughout the system structure; a fast parametrization of the potential without the necessity of reference simulations; and the straightforward usage of the model on the most commonly used molecular dynamics platforms. The method is presented and validated with two case studies, the enzyme adenylate kinase and the therapeutic antibody pembrolizumab, by comparing the results obtained with the CANVAS model against fully atomistic simulations. The modeling software, implemented in Python, is made freely available for the community on a collaborative github repository.

Capturing the influence of a crowded environment on protein GB1 dimerization at coarse-grained resolutions.

In-cell protein-protein association, which is crucial in enzyme catalysis and polymerization, occurs in an environment that is highly heterogeneous and crowded. The crowder molecules exclude the reactant molecules from occupying certain regions of the cell, resulting in changes in the reaction thermodynamics and kinetics. Recent studies, both experiment and simulations, also revealed that the nature of interaction between crowder and protein species, in particular the soft interactions, plays an important role in crowder induced effects on protein association. To this end, from a simulation perspective, it is important to decipher the level of structural resolution in a protein-crowder model that can faithfully capture the influences of crowding on protein association. Here we investigate a model protein system in a dilute medium as well as in a crowded medium, adopting two structural resolutions of the constituent molecules, and using either molecular or Langevin dynamics to propagate the equations of motion. In particular, we look at dimerization of protein GB1, since GB1 dimers can exist in both side-by-side and domain-swapped conformations, showing the important role of residue-level structural details. The free energy change in protein association in a crowded solution relative to a crowder free medium obtained from the simulations is then compared with experimental results. The results reveal the pros and cons of adopting different resolution approaches.

Rapid coarse-grained simulation of whole cryo-tomograms.

Cryo-electron tomography is a rapidly expanding field of microscopy investigation, from subtomogram averaging to cellular tomography. This range of applications leads to highly varied data which can be difficult to analyze systematically. Multiple deep-learning techniques have been successfully applied to automate object classification, aiding in particle averaging or structural analysis. However, these neural networks typically rely on large training datasets that are painstakingly generated by hand based on a subset of the data. A method that does not require detailed hand-segmentation by an expert is needed to allow high-throughput application of deep-learning tools to varied datasets. Here we present CTS, a toolbox for modeling vitreous fields of randomized proteins and other organic structures and simulating these fields as complete tomograms. Crucially, CTS also generates a molecular atlas that provides efficient inputs to deep learning tools. This simple-to-use program can generate a complete simulation and atlas in minutes, and accepts a wide range of structure files to generate independent training data. The simulation replicates imaging processes to produce a realistic level of CTF aberration, resolution, contrast, and noise to best provide high-quality training data that is still representative of target data. CTS can model multiple structural organizations, such as bundled fibers, uniform and non-uniform protein complexes, and particle clusters in addition to modeling carbon gridholes, vitreous ice, and vesicular membranes. These simulations have proven to be capable of training highly accurate deep-learning segmentations of acellular tomograms with no further human input. Complex cellular tomograms are segmented more reliably and with less manual training required when paired with simulated training datasets, in addition to improving accuracy especially with fine-grained structural differences that can be difficult to discern and segment by hand. This tool therefore lowers the barrier for broad and novel deep-learning applications in cryo-ET.

Optimization of hydrogen-deuterium exchange ensemble reweighting (HDXer) to aid the biophysical characterization of proteins.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) experiments provide unique insights into native protein structure and dynamics in solution but have limited spatial resolution. Molecular dynamics (MD) simulations, in contrast, allow for the exploration of protein conformational landscapes with an atomistic level of resolution. However, forcefield inaccuracies, crystallographic biases, and timescale limitations may compromise the validity of the final structural ensemble. Hydrogen Deuterium eXchange Ensemble Reweighting (HDXer) is a maximum entropy approach that was recently developed to (1) quantitatively predict hydrogen-deuterium exchange from a modeled conformational ensemble and (2) refine the candidate ensemble to best fit an experimental HDX-MS dataset. Here, we compare how various computational and experimental parameters impact the performance of HDXer, using extracellular regulated kinase 2 (ERK2) and hen egg white lysozyme (HEWL) as model proteins. Conventional molecular dynamics simulations of ERK2 and HEWL were performed in triplicate using the CHARMM36m, AMBER FF19SB, and Drude forcefields. After predicting the HDX rates of each candidate ensemble, HDXer was used to fit the simulated structures to the corresponding HDX-MS datasets. To assess the influence of simulation length, truncated and strided copies of the full-length simulations were generated. Other factors, including peptide charge state selection, ion mobility mass spectrometry, and independent HDX-MS data acquisitions, were also tested. We find that while some variables (e.g., high vs. low peptide charge states) do not have a significant effect, other parameters (e.g., early vs. late timepoint selection) directly correlate with the disagreement between calculated and experimental deuterium uptake. The results from this benchmarking inform the development of a recommended HDXer workflow and best practices for optimal data acquisition.

Performance of Artificial Neural Network and Modified Gravitational Search Algorithm Models to Predict Vibration Response of Geocell Reinforced Based Weak Sand

The use of a fast evolving artificial intelligence technology (AIT) to forecast the vibration response of maritime soil based on geocells is explored in this research. The vibration response is represented by an indicator called peak particle velocity (PPV). For the purpose of predicting PPV, the artificial intelligence techniques Artificial Neural Network (ANN) and Modified Gravitational Search Algorithm (MGSA) are employed. To create the dataset for the model, a number of field vibration tests were first performed over the geocell-reinforced beds. PPV variation was investigated by varying the test variables—footing embedment, dynamic load, infill material modulus, width, and depth of geocell mattress placement—during the test performed. The various statistical indicators were determined in order to evaluate the prediction performance of a constructed model. Plate load results on geocell-reinforced foundation beds have been used to validate the proposed hybrid ANN-MGSA model. High accuracy and consistency were found when the findings of the ANN-EHO, JSA, MOA, and RNN method were compared, particularly at predicted and actual resolution levels. A parametric sensitivity has also been examined in order to better understand the behaviour of geocell-reinforced structures.

Review on AI-based Smart Farming of Leafy Vegetables using Hydroponics System

Agriculture plays a very important role in the development of a nation. By 2050, the human population is predicted to achieve eight.9 billion associate degrees an increased population needs adequate offer of contemporary turnout. This downside is often resolved by sensible agriculture and fashionable implementation exploitation net of Things (IoT) technologies. Hydroponics may be a soil-less cultivation that uses minerals from nutrient resolution. The parameters to be thought-about for hydroponic systems embrace pH scale level, electrical conduction (EC), dissolved atomic number 8 (DO), water temperature, water rate of flow, and water level of nutrient resolution. There are half-dozen varieties of the hydroponic system: nutrient film technique (NFT), drip system, water culture, ebb, and flow system, geophonic and wick.

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study.

There were an estimated 100,306 drug overdose deaths between April 2020 and April 2021, a three-quarter increase from the prior 12-month period. There is an approximate 6-month reporting lag for provisional counts of drug overdose deaths from the National Vital Statistics System, and the highest level of geospatial resolution is at the state level. By contrast, public social media data are available close to real-time and are often accessible with precise coordinates. The purpose of this study is to assess whether county-level overdose mortality burden could be estimated using opioid-related Twitter data. International Classification of Diseases (ICD) codes for poisoning or exposure to overdose at the county level were obtained from CDC WONDER. Demographics were collected from the American Community Survey. The Twitter Application Programming Interface was used to obtain tweets that contained any of the 36 terms with drug names. An unsupervised classification approach was used for clustering tweets. Population-normalized variables and polynomial population-normalized variables were produced. Furthermore, z scores of the Getis Ord Gi clustering statistic were produced, and both these scores and their polynomial counterparts were explored in regression modeling of county-level overdose mortality burden. A series of linear regression models were used for predictive modeling to explore the interpretability of the analytical output. Modeling overdose mortality with normalized demographic variables alone explained only 7.4% of the variability in county-level overdose mortality, whereas this was approximately doubled by the use of specific demographic and Twitter data covariates based on a backward selection approach. The highest adjusted R2 and lowest AIC (Akaike Info Criterion) were obtained for the model with normalized demographic variables, normalized z scores from geospatial analyses, and normalized topic counts (adjusted R2=0.133, AIC=8546.8). The z scores of the Getis Ord Gi statistic appeared to have improved utility over population-normalization alone. In this model, median age, female population, and tweets about web-based drug sales were positively associated with opioid mortality. Asian race and Hispanic ethnicity were significantly negatively associated with county-level burdens of overdose mortality. Social media data, when transformed using certain statistical approaches, may add utility to the goal of producing closer to real-time county-level estimates of overdose mortality. Prediction of opioid-related outcomes can be advanced to inform prevention and treatment decisions. This interdisciplinary approach can facilitate evidence-based funding decisions for various substance use disorder prevention and treatment programs.

Shell proteins and microstructural analysis identify the origin of shell arts with species resolution in pearl oysters

Molluscan shells are diverse, beautiful, and mechanically robust. They were commonly used by humans during ancient times as money, decorative objects, or functional tools. Luodian, a unique shell art in China dating back 3000 years, has served as a cultural carrier between China and Europe. To understand the production processes as well as underlying cultural contexts, it is important to identify the material origin of the art. This study combined proteomics and microstructural analysis to identify the raw material origins of three Luodian samples from the Qing Dynasty with species resolution and potential maturity levels. Shell proteomics and multiple sequence alignment have identified key shell matrix proteins (N66 and Pif) from Pinctada maxima, a pearl oyster distributed in the Indo-Pacific region. Although other factors (temperature, salinity, etc.) may contribute to the thickness of nacreous tablets in the shell, the thickness is inversely proportional to the shell size in the pearl oyster. We inferred the maturity of raw materials in different Luodian samples, which are tailored to different purposes. This study provides a framework and foundation for identifying the origin of various shell arts, especially those made from pearl oysters.