Different Types Of Heterogeneity Research Articles

Cell Penetrating Peptides (CPPs) in drug delivery; a new era should be started

This article focuses on the promising roles of cell penetrating peptides (CPPs) in local drug delivery. Cell penetrating peptides have vast applications in drug delivery, however the mechanisms of cellular uptake is still a controversy topic. The challenges in determining the exact cellular uptake hinders further understanding of the fate of these peptides. These challenges include the lack of a reproducible way to ascertain the mechanism of entry. In addition, the in-vitro work on cell-lines did not end in definite answer to the cellular uptake mechanism. Indeed, the unreliability of such work seems logical due to different reasons, including the cell line-dependent uptake and the cell membrane heterogeneity of different types of cells. Although several CPPs reached the clinical trials, no one could reach the market, yet. Re-evaluation era should be started through moving forward for more researches on the roles of CPPs in Local delivery to homogeneous cell population of specific tissue or organ of the body. While this article demonstrates an example on variability in cellular uptake in different cell lines, it encourages more sophisticated work to be conducted on CPPs for ocular, nasal, transdermal, buccal and even inner ear delivery.

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.

Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifier's reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations are present, and could successfully remove batch effects within the same population. We also showed that the machine learning classifier's prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches.

Research on the Impact of ESG Rating on Enterprise Growth

ESG transformation is profoundly changing corporate goals and business logic, improving the growth potential of enterprises. This article selects data from Chinese A-share listed companies from 2011 to 2021 as research samples to empirically test the impact of ESG rating on enterprise growth and its impact mechanism. Research has found that ESG rating can significantly enhance the growth of enterprises. Meanwhile, the impact of ESG rating on enterprise growth exhibits heterogeneity in different types of enterprises and regions with different degrees of marketization. From the perspective of impact mechanism, ESG rating can promote high-quality development of enterprises and improve enterprise growth by improving financial flexibility and alleviating financing constraints. This article provides relevant theoretical evidence for the practice of implementing the ESG rating in enterprises and it has important practical guiding significance for the government to formulate enterprise information disclosure and ESG evaluation systems, as well as for China to further promote high-quality transformation and growth of the macro economy.

MMH-STA: A Macro-Micro-Hierarchical Spatio-Temporal Attention Method for Multi-Agent Trajectory Prediction in Unsignalized Roundabouts

Simultaneously predicting the future trajectories of heterogeneous multiple agents in a neighborhood is crucial for ensuring the safe and efficient operations of autonomous vehicles in intelligent transportation systems. The unsignalized roundabout is a typical traffic scenario wherein the multivariate and complex information interactions make the prediction task challenging. To address the aforementioned challenges, a macro-micro-hierarchical spatio-temporal attention (MMH-STA) architecture, which can effectively extract the temporal and spatial features of multiple agents based on the interaction mechanism, is presented in this paper. This work makes three contributions: 1) A novel hierarchical framework, which considers the heterogeneity of different types of agents, is proposed for trajectory prediction in the roundabout environment. Similarly, the macro-state for interaction with the roundabout structure and the micro-state for interaction with other agents are introduced for an agent. 2) A heterogeneous graph is devised to represent the spatial interactions of a multi-agent, which is reflected in connections between different types of agents and the properties of nodes and edges in the graph. 3) A novel heterogeneous graph attention network with a multi-order neighborhood is designed to describe the spatial feature interactions in the neighborhood. Finally, a characterized decoder forecasts the future trajectories of multiple agents concomitantly. The experimental results reveal that the proposed model can effectively implement multi-agent trajectory prediction in roundabout scenarios with high accuracy and state-of-the-art performance compared to the baseline.

Coordinated development and driving factor heterogeneity of different types of urban agglomeration carbon emissions in China.

Carbon emission (CE) reduction has become the primary task of China's urban agglomerations (UAs) in achieving sustainable development goals. This paper uses a decoupling model and coupling coordination model to measure the relationship between the development levels of different types of UAs and CEs in China from 2004 to 2016. Concurrently, the geographically and temporally weighted regression model is used to explore the spatial heterogeneity of the impact of different driving factors on the CEs of UAs. The results show the following: Most UAs have the potential to further decouple CEs and economic growth. Most UAs are still in coordinated development (> 0.5). Among the service innovation UAs, the Yangtze River Delta UA has a coupling coordination of less than 0.3, while the Pearl River Delta UA has a coupling coordination of more than 0.8, showing polarization. Manufacturing and resource-based UAs are still in the grinding adaptation stage (0.5-0.8). There are apparent spatiotemporal differences in the impacts of various driving factors on the CE of UAs. The level of land urbanization and investment in fixed assets promote CEs. However, the level of population urbanization and industrial structure restrain CEs. Therefore, reducing land development and industrial transformation can be an effective means to reduce CEs in UAs. These findings will provide extensive insights for different UAs to achieve differentiated low-carbon development.

Large-scale experimental study of heterogeneity in different types of hydrate reservoirs by horizontal well depressurization method

The heterogeneity of hydrate distribution and decomposition in sandy reservoirs is an important issue. Because large hydrate simulators are scarce and visualization difficult, heterogeneity in large-scale hydrate reservoirs has been consistently underestimated. In this study, a pilot-scale hydrate simulator with an effective volume of 117.8 L was used to simulate the horizontal well depressurization decomposition of two different types of hydrate reservoirs: gas-saturated and water-saturated. The production performance and heterogeneity of these two hydrate reservoirs were analyzed during the hydrate decomposition process. For the first time, hydrate decomposition heterogeneity was classified into planar heterogeneity and interlayer heterogeneity based on the heterogeneous characteristics in the horizontal and vertical directions. Based on the pressure difference and duration, a dimensionless factor was proposed to quantify the intensity of the heterogeneity at a specific location. Furthermore, this study related the hydrate decomposition heterogeneity intensity variation process to the variations in the two-dimensional geometric model for hydrate distribution in the pore space. The experimental results showed that horizontal well extraction using the depressurization method was highly efficient. During the hydrate decomposition process, planar heterogeneity was stronger in the water-saturated hydrate reservoir than in the gas-saturated hydrate reservoir. In gas-saturated hydrate reservoir, hydrate saturation and heterogeneity increased from top to bottom. In water-saturated hydrate reservoir, hydrate saturation and interlayer heterogeneity increased first and then decreased from top to bottom. The study of heterogeneity was critical for optimizing the hydrate extraction scheme, and emphasis should be placed on the placement of production enhancement facilities in regions with significant heterogeneity.

Does the Photovoltaic poverty alleviation project promote county economic development? : Evidence from 852 counties in China

Photovoltaic poverty alleviation projects (PVPA), an innovative and unique policy introduced by China to promote green development and poverty alleviation, has attracted growing attention from both the public and the government. However, there is no consistent conclusion in the existing literature on the question of whether the establishment of PVPA has promoted county economic development. Based on the panel data of 852 counties from 12 provinces in China, this study employs the quasi-experimental method of Difference in Difference with Propensity Score Matching (PSM-DID) to examine the effects of the establishment of PVPA on county economy. It is found that: (1) The introduction of PVPA can significantly and sustainably promote the county economy; the longer the PVPA is introduced, the greater the boost to economic development. These results have been verified by robustness testing. (2) The policy effects of PVPA show heterogeneity in different types of counties. Specifically, the policy effects are stronger in non-national-level poor counties than in national-level poor counties, while the positive policy effects are more prominent in western counties than in non-western counties. (3) The policy effects of PVPA have positive but weak spillover effects on the neighboring areas. Based on the above findings, suggestions are proposed, such as sustainable operation of PVPA through multiple channels and enhanced cooperation of photovoltaic projects between neighboring regions are proposed.

Measuring spatio-temporal heterogeneity and interior characteristics of green spaces in urban neighborhoods: A new approach using gray level co-occurrence matrix

Urban green space (UGS) is a complex and highly dynamic interface between people and nature. The existing methods of quantifying and evaluating UGS are mainly implemented on the surface features at a landscape scale, and most of them are insufficient to thoroughly reflect the spatial-temporal relationships, especially the internal characteristics changes at a small scale and the neighborhood spatial relationship of UGS. This paper thus proposes a method to evaluate the internal dynamics and neighborhood heterogeneity of different types of UGS in Leipzig using the gray level co-occurrence matrix (GLCM) index. We choose GLCM variance, contrast, and entropy to analyze five main types of UGS through a holistic description of their vegetation growth, spatial heterogeneity, and internal orderliness. The results show that different types of UGS have distinct characteristics due to the changes of surrounding buildings and the distance to the built-up area. Within a one-year period, seasonal changes in UGS far away from built-up areas are more obvious. As for the larger and dense urban forests, they have the lowest spatial heterogeneity and internal order. On the contrary, the garden areas present the highest heterogeneity. In this study, the GLCM index depicts the seasonal alternation of UGS on the temporal scale and shows the spatial form of each UGS, being in line with local urban planning contexts. The correlation analysis of indices also proves that each type of UGS has its distinct temporal and spatial characteristics. The GLCM is valid in assessing the internal characteristics and relationships of various UGS at the neighborhood scales, and using the methodology developed in our study, more studies and field experiments could be fulfilled to investigate the assessment accuracy of our GLCM index approach and to further enhance the scientific understanding on the internal features and ecological functions of UGS.

Complex ontology alignment for autonomous systems via the Compact Co-Evolutionary Brain Storm Optimization algorithm

Autonomous Systems (ASs) that work in the open, dynamic environment are required to share their data entities and semantics to implement the co-operations. Typically, AS’s data schemas and semantics are described via ontology. Since ASs need to maintain their autonomy and conceptual specificity, their ontologies might define one concept with different terms or in different contexts, which yields the heterogeneity issue and hampers their co-operations. An effective solution is to establish a set of data entity’s correspondences through the Ontology Alignment (OA). Sine the simple correspondence of one-to-one style lacks expressiveness and cannot completely cover different types of heterogeneity, ASs’ co-operations require using the complex correspondence of one-to-many or many-to-may style. Inspired by the success of applying the Brain Storm Optimization algorithm (BSO) to solve diverse complex optimization problems, this work proposes a Compact Co-Evolutionary BSO (CCBSO) to face the challenge of aligning AS ontologies. In particular, the AS ontology aligning problem is formally defined, a hybrid confidence measure for distinguishing the simple and complex correspondences is proposed, and a problem-specific CCBSO is presented. The experiment tests CCBSO’s performance on different AS ontology aligning tasks, which consist of two simple ontology aligning tasks and one complex ontology aligning track. The experimental results show that CCBSO outperforms the state-of-the-art ontology aligning techniques on all simple and complex ontology aligning tasks.

Pathological Osteoclasts and Precursor Macrophages in Inflammatory Arthritis.

Macrophages comprise a variety of subsets with diverse biological functions, including inflammation, tissue repair, regeneration, and fibrosis. In the bone marrow, macrophages differentiate into multinucleated osteoclasts, which have a unique bone-destroying capacity and play key roles in physiological bone remodelling. In contrast, osteoclasts are also involved in inflammatory bone erosion in arthritis and it has been unclear whether the osteoclasts in different tissue settings arise from similar monocytoid precursors and share similar phenotypes. Rapid progresses in the sequencing technologies have provided many important insights regarding the heterogeneity of different types of osteoclasts. The application of single-cell RNA sequencing (scRNA-seq) to the osteoclast precursor-containing macrophages enabled to identify the specific subpopulation differentiating into pathological mature osteoclasts in joints. Furthermore, an intravital imaging technology using two-photon microscopy has succeeded in visualizing the real-time dynamics of immune cells in the synovial microenvironment. These technologies together contributed to characterize the unique macrophages in the inflamed synovium, termed “arthritis-associated osteoclastogenic macrophages (AtoMs)”, causing the pathological bone destruction in inflammatory arthritis. Here, we review and discuss how novel technologies help to better understand the role of macrophages in inflammatory arthritis, especially focusing of osteoclastogenesis at the pannus-bone interface.

Heterogeneity-induced lane and band formation in self-driven particle systems

The collective motion of interacting self-driven particles describes many types of coordinated dynamics and self-organisation. Prominent examples are alignment or lane formation which can be observed alongside other ordered structures and nonuniform patterns. In this article, we investigate the effects of different types of heterogeneity in a two-species self-driven particle system. We show that heterogeneity can generically initiate segregation in the motion and identify two heterogeneity mechanisms. Longitudinal lanes parallel to the direction of motion emerge when the heterogeneity statically lies in the agent characteristics (quenched disorder). While transverse bands orthogonal to the motion direction arise from dynamic heterogeneity in the interactions (annealed disorder). In both cases, non-linear transitions occur as the heterogeneity increases, from disorder to ordered states with lane or band patterns. These generic features are observed for a first and a second order motion model and different characteristic parameters related to particle speed and size. Simulation results show that the collective dynamics occur in relatively short time intervals, persist stationary, and are partly robust against random perturbations.

Pediatric Single Cell Cancer Atlas: An Integrative Web-Based Resource for Single Cell Transcriptome Data from Pediatric Leukemias

Introduction: The emergence and optimization of single cell profiling as a powerful tool to characterize the tumor microenvironment has revealed the heterogeneity of pediatric cancers, particularly different leukemia types/subtypes. Ease of access and analysis of the data from studies on different leukemia types is critical for improving diagnosis as well as therapy.Currently, there are no single cell data based resources available for pediatric leukemias. We have developed a comprehensive resource, Pediatric Single Cell Cancer Atlas (PedScAtlas), with the goal of developing a pan-leukemia genomics signature as well as highlighting the heterogeneity of different types of leukemia. This resource facilitates exploration and visualization of expression signatures in different leukemias without requiring extensive analysis and bioinformatics support.Methods: The PedScAtlas was built based on single cell data from various leukemias and normal bone marrow (BM) cells that have been pre-processed and analyzed using a uniform approach to generate normalized expression data (M. Bhasin et al. Blood 2020 (ASH), S. S. Bhasin et al. Blood 2020 (ASH), Panigraphy et al. JCI 2019, Stroopinsky et al. Haematologica 2021, Thomas et al. Blood 2020 (ASH)). The current version of PedScAtlas contains data from 30 local leukemia samples (Bhasin, et al. Blood 2020 (ASH), Thomas et al. Blood 2020 (ASH)) and those available on public resources such as GEO (Bailur et al. JCI Insight 2020). The PedScAtlas dataset and the Immune cell dataset each underwent quality control, integration, normalization, and dimensionality reduction using the Uniform Manifold Approximation and Projection (UMAP) method. Unsupervised UMAP analysis identified cellular clusters with similar transcriptome profiles that were annotated based on expression of cell-specific markers. Differential expression comparing different types of leukemia including acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), T-cell acute lymphoblastic leukemia (T-ALL), and mixed phenotype acute leukemia (MPAL) samples was performed. To further ascertain genes specifically expressed in malignant blasts, the atlas also contains data from normal BM samples (Bailur et al. JCI Insight 2020) and healthy immune cells from the census of Immune Cells by the Human Cell Atlas Project (https://data.humancellatlas.org/). The web resource source code is written in R programming language and the interactive webserver has been implemented using the R Shiny package (Fig 1). The tool has been extensively tested on multiple operating systems (Linux, Mac, Windows) and web-browsers (Chrome, FireFox, and Safari). The tool is currently hosted on a 64bit CentOS 6 backend server running the Shiny Server program designed to host R Shiny applications.Results: The PedScAtlas contains data that facilitate exploration of gene expression profiles across leukemia types/subtypes and tumor microenvironment (TME) cell types. The atlas includes data from 33,930 AML, 25,744 T-ALL, 13,404 MPAL, and 6,252 B-ALL blast cells. It also contains single cell profiles of healthy BM samples from publicly available studies. The user can select data sets from the 5 major types of leukemia and normal BM in any combination of their choice to explore the expression profile of a gene of interest. The data can be visualized as UMAP (Fig. 1), or violin plots with annotations based on cluster ID, cell type, disease type, sample ID, and future continuous remission or relapse outcome. The UMAP with gene expression analyses allows the user to visualize the distribution of cell expressing a given gene on the UMAP plot. The Biomarker tool shows expression of different leukemia biomarker gene sets in the entire leukemia dataset. The Immune Cell section contains BM data from the Human Cell Atlas Project; the purpose of this section is to validate leukemia biomarkers by checking that the gene does not have significant expression in the healthy immune microenvironment.Conclusions: The PedScAtlas resource provides a unique and straightforward tool for biomarker identification, analysis of leukemia subtype heterogeneity, and transcriptome profile of the immune cell microenvironment. The resource is available online at https://bhasinlab.bmi.emory.edu/PediatricSC/. [Display omitted] DisclosuresDeRyckere: Meryx: Other: Equity ownership. Graham: Meryx: Membership on an entity's Board of Directors or advisory committees, Other: Equity ownership.

Single cell heterogeneity in human pluripotent stem cells

Human pluripotent stem cells (hPSCs) include human embryonic stem cells (hESCs) derived from blastocysts and human induced pluripotent stem cells (hiPSCs) generated from somatic cell reprogramming. Due to their self-renewal ability and pluripotent differentiation potential, hPSCs serve as an excellent experimental platform for human development, disease modeling, drug screening, and cell therapy. Traditionally, hPSCs were considered to form a homogenous population. However, recent advances in single cell technologies revealed a high degree of variability between individual cells within a hPSC population. Different types of heterogeneity can arise by genetic and epigenetic abnormalities associated with long-term in vitro culture and somatic cell reprogramming. These variations initially appear in a rare population of cells. However, some cancer-related variations can confer growth advantages to the affected cells and alter cellular phenotypes, which raises significant concerns in hPSC applications. In contrast, other types of heterogeneity are related to intrinsic features of hPSCs such as asynchronous cell cycle and spatial asymmetry in cell adhesion. A growing body of evidence suggests that hPSCs exploit the intrinsic heterogeneity to produce multiple lineages during differentiation. This idea offers a new concept of pluripotency with single cell heterogeneity as an integral element. Collectively, single cell heterogeneity is Janus-faced in hPSC function and application. Harmful heterogeneity has to be minimized by improving culture conditions and screening methods. However, other heterogeneity that is integral for pluripotency can be utilized to control hPSC proliferation and differentiation.

Does investor sentiment differently affect stocks in different sectors? Evidence from China

PurposeThis paper aims to investigate the dynamic relationship between the investor sentiment and the return of various sectors in the Chinese stock market.Design/methodology/approachThe wavelet coherence and wavelet phase angle approaches are used to study the lead–lag associations between sentiment index and stock returns in a time–frequency way. The multiscale linear and nonlinear Granger causality tests are performed to explore whether there is a causality between them.FindingsThe empirical results show that during normal period, investor sentiment index has a stronger relationship with stock returns of industrials, consumer discretionary, health care, utilities, real estate and financial sectors. In crisis period, investor sentiment has a significant positive relationship with all industry sectors. In the short term, there is bidirectional causality between investor sentiment and stock returns of all sectors. In the medium and long run, almost all sector stock returns Granger-cause the investors' sentiment index but investor sentiment does not Granger-cause all sectors, which is in contrast to the developed markets.Practical implicationsThe interindustry impact of investment sentiment on the stock market can help construct arbitrage portfolio by investors who are interested in Chinese stock market.Originality/valueThis paper focuses on the industry sector differences of investor sentiment impact on the Chinese stock market. As far as the authors know, this is the first paper to explore the time–frequency relationship between sentiment index and industry stock returns in China using the time–frequency method based on wavelet coherence, which considers the heterogeneity of different types of investors' responses to various economic and financial events.

Analysis of spatial distribution of touristic accommodation in Poland with the kernel density estimation of POIs

Motives: Using Points-of-Interest (POIs) data and GIS software, the spatial heterogeneity of different types of accommodation could cheap, easily and quick be analyzed. Aim: The use of kernel density estimation (KDE) of Points-of-Interest data to shown spatial distribution of different types of accommodation in Poland. Results: There is a close relationship between the type of accommodation and the type of tourist attraction.

Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics.

In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population's heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population's heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations.

Impact of the 21-Gene Recurrence Score(Oncotype DX®) on adjuvant therapy decision-making: a collaborative multicenter cohort study from Argentina

Introduction: At present, more than half of patients diagnosed with early-stage breast cancer (BC) and express hormonal receptors will receive some adjuvant chemotherapy scheme, but only a few of them would benefit in terms of survival. Genomic platforms allow a better understanding of the heterogeneity of different types of hormonal receptor-positive and HER2-negative BC. They have proven their validity as tools to identify those patients who will obtain a clear benefit with the indication of chemotherapy treatment. The aim of this study is to analyze the use of the genomic platform, namely, Oncotype Dx® and its impact on the indication of adjuvant treatment, evaluated mainly as the change in treatment indication. Methods: Multicenter observational cohort study was performed in different Mastology units in Argentina. Patients underwent the Oncotype Dx to clarify the adjuvant treatment. Treatment decisions were settled before and after performing Oncotype Dx. Results: From January 2013 to December 2018, 211 patients with luminal A or B and HER2-negative breast carcinomas, who underwent the Oncotype Dx, were included. Based on our records, 40% of the patients change the indication of adjuvant treatment after the performance of the Oncotype Dx. Of these, 24% of patients who underwent initial endocrine therapy only adjusted their treatment with the addition of chemotherapy. Among patients with an initial CTH recommendation, 49% were able to receive endocrine therapy only when, due to traditional prognostic factors, they would have received chemotherapy. Conclusions: In our population, the use of the Recurrence Score was clinically significant in relation to the change of the established treatments. Consequently, it is a very important tool and a decisive factor in the adjuvant indication in patients with positive hormonal receptors and HER2neu-negative early BC.

Study on Heterogeneity of Pore Throats at Different Scales and Its Influence on Seepage Capacity in Different Types of Tight Carbonate Reservoirs

The carbonate reservoirs in the middle Sichuan area have undergone complicated tectonics, resulting in various types of reservoir space, large secondary changes, and multiple complexities. Taking the tight carbonate gas reservoir of the Deng-4 member in this area as an example, based on casting thin sections, scanning electron microscopy, and high-pressure mercury injection experiments, the reservoir space and microstructural characteristics of the micropore throats were studied, and the influence of the microscale heterogeneity in different types of reservoirs on the seepage capacity was analyzed by applying fractal theory. The results showed that the reservoir space in the tight carbonate rock of the Deng-4 member in the study area could be divided into 3 types: pore-hole-fracture, pore-hole, and pore types. The distribution characteristics of the pore throat diameter were multimode wide type, double-mode high and low asymmetrical type, and single-mode concentrated type. The fractal dimension and seepage capability of the pore throat increased successively in sizes from less than 0.1 μm to 0.1~1.0 μm and greater than 1.0 μm. On the one hand, the development of karst caves and fractures controlled the percolation ability of tight carbonate reservoirs; on the other hand, it enhanced the heterogeneity of the micropore throat structure. However, the development degree of dissolved pores and microfractures has a weak contribution to the connectivity and seepage capacity of the reservoir space. Acidification, fracturing, and other measures can be adopted to enhance the connectivity between pores to improve the productivity of the gas reservoir. This study provides a scientific basis for the efficient exploration and development of tight carbonate reservoirs.

Effects of livestock grazing on plant species diversity vary along a climatic gradient in northeastern Iran

AbstractQuestions(a) Do patterns of species diversity and composition differ between plots exposed to high vs. low grazing intensity? (b) Do these differences vary among three regions with different climates, representing a gradient from arid to sub‐humid conditions?LocationNortheastern Iran.MethodsWe compared plots intensively grazed by sheep and goat (HG) to plots with low grazing intensity (LG), across three climatic regions: arid, semi‐arid and sub‐humid. Species diversity (expressed as Shannon H) was compared using linear mixed‐effect models. Dissimilarity analysis was used to estimate the compositional heterogeneity of different types of plots, reflecting the differences in species composition. Indicator Species Analysis was used to identify individual species associated with particular types of plots.ResultsThe HG plots had lower values of Shannon H than LG plots, across all climatic regions. Further, the effect of grazing on species diversity varied between the climatic regions, with the most pronounced differences between the HG and LG plots in the sub‐humid region. Although no differences in species diversity were detected between the three climatic regions, compositional heterogeneity was the highest in the sub‐humid region and the lowest in the semi‐arid region, with the most pronounced differences between the HG and LG plots in the sub‐humid region. Indicator Species Analysis showed significant main effects of both climate and grazing on species composition, revealing species that respond to grazing in each of the three climatic regions.ConclusionsOvergrazing was detected to reduce the diversity of species in all three climatic regions, even though its effect was most pronounced in the sub‐humid region. Therefore, the control of overgrazing or reduction of its intensity appears an effective management tool for protecting species diversity, applicable in areas with different climates.

Detection of heterogeneity on multi-spectral transmission image based on multiple types of pseudo-color maps

In transmission imaging, there are many difficulties in identifying heterogeneity on multi-spectral transmission images due to the strong absorption and scattering of biological tissues. This paper combines the multi-wavelength fusion information with the Faster-RCNN model to achieve the heterogeneous detection on the multi-spectral fusion pseudo-color map. Firstly, the original multi-spectral transmission images of phantom are collected on the self-built experimental platform. Then, the gray-scale level of image is improved by modulation and frame accumulation technique. Thereby, fusion pseudo-color maps (fusion pseudo-color maps of original RGB and gradient image) with high quality are obtained. And by comparing the running time, mean average precision (mAP) and root mean square error (RMSE) of Faster-RCNN model, the optimal iteration times and learning rate of model are determined. Finally, the fusion pseudo-color maps used for testing are input into the best Faster-RCNN model to realize the heterogeneous detection. The results show that the Faster-RCNN model established in this paper can effectively identify 2, 4 and 7 different types of heterogeneity on multi-spectral images, respectively. And the best mAP detection results for 2, 4 and 7 heterogeneities are 97.65%, 96.78% and 97.04%, respectively. In conclusion, this paper achieves the detection of heterogeneity on multi-spectral transmission images by fusing high-quality multi-wavelength transmission images and the Faster-RCNN network model, which will promote the clinical application of transmission imaging in early screening of breast cancer.