Suitable Model Research Articles

Abstract 5339: Establishment of a humanized IL5/IL5RA mouse model to evaluate modulators of eosinophilic inflammation in vivo

Abstract IL-5/IL-5R signaling plays a key role in the maturation, activation, survival, and recruitment of eosinophils to sites of inflammation. Targeting this pathway is an attractive therapeutic strategy for certain inflammatory diseases, such as asthma. Here, we generated a novel humanized IL5/IL5RA model (B-hIL5/hIL5RA) to examine the utility of human IL-5/IL5R-targeting therapeutic candidates. Specifically, the entire murine Il5 coding region was replaced by the corresponding human IL5 sequence, and the human IL5RA sequence coding the extracellular domain was inserted to replace the corresponding murine sequence. Successful generation of the model was confirmed by expression of human IL-5 by ELISA and IL5RA by flow cytometry. Furthermore, the proportion of eosinophils in the blood of B-hIL5/hIL5RA mice increased after stimulation with human IL5 recombinant protein, indicating that the humanized IL5RA protein is functional. Next, the ability of the humanized mice to establish asthma-like disease was tested using ovalbumin: following a 14 day sensitization period, mice were challenged with nebulized OVA daily from day 21 until the endpoint; treatment with mepolizumab or benralizumab analog was performed. Flow cytometric analysis of the bronchioalveolar fluid indicates that treatment with mepolizumab or benralizumab analog significantly lowered the number of eosinophils when compared to the isotype control in homozygous B-hIL5/hIL5RA mice. Blood counts and quantification of lung H&E sections also confirm that the treatments lowered the number of eosinophils in circulation and lung tissue, respectively. Taken together, these data indicate that B-hIL5/hIL5RA mice are a suitable preclinical model to test novel modulators of human IL-5/IL-5RA signaling. Citation Format: Suman Zhao, Shujin Zhang, Zhen Chen, Liya Yang, Mari Kuraguchi, Jiangfeng Yuan, Heiling Shen. Establishment of a humanized IL5/IL5RA mouse model to evaluate modulators of eosinophilic inflammation in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5339.

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Photocatalysts have made great contributions to the degradation of pollutants to achieve environmental purification. The traditional method of developing new photocatalysts is to design and perform a large number of experiments to continuously try to obtain efficient photocatalysts that can degrade pollutants, which is time-consuming, costly, and does not necessarily achieve the best performance of the photocatalyst. The rapid development of photocatalysis has been accelerated by the rapid development of artificial intelligence. Intelligent algorithms can be utilized to design photocatalysts and predict photocatalytic performance, resulting in a reduction in development time and the cost of new catalysts. In this paper, the intelligent algorithms for photocatalyst design and photocatalytic performance prediction are reviewed, especially the artificial neural network model and the model optimized by an intelligent algorithm. A detailed discussion is given on the advantages and disadvantages of the neural network model, as well as its application in photocatalysis optimized by intelligent algorithms. The use of intelligent algorithms in photocatalysis is challenging and long term due to the lack of suitable neural network models for predicting the photocatalytic performance of photocatalysts. The prediction of photocatalytic performance of photocatalysts can be aided by the combination of various intelligent optimization algorithms and neural network models, but it is only useful in the early stages. Intelligent algorithms can be used to design photocatalysts and predict their photocatalytic performance, which is a promising technology.

A prognostic model for Schistosoma japonicum infection-associated liver hepatocellular carcinoma: strengthening the connection through initial biological experiments

BackgroundNumerous studies have shown that Schistosoma japonicum infection correlates with an increased risk of liver hepatocellular carcinoma (LIHC). However, data regarding the role of this infection in LIHC oncogenesis are scarce. This study aimed to investigate the potential mechanisms of hepatocarcinogenesis associated with Schistosoma japonicum infection.MethodsBy examining chronic liver disease as a mediator, we identified the genes contributing to Schistosoma japonicum infection and LIHC. We selected 15 key differentially expressed genes (DEGs) using weighted gene co-expression network analysis (WGCNA) and random survival forest models. Consensus clustering revealed two subgroups with distinct prognoses. Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression identified six prognostic DEGs, forming an Schistosoma japonicum infection-associated signature for strong prognosis prediction. This signature, which is an independent LIHC risk factor, was significantly correlated with clinical variables. Four DEGs, including BMI1, were selected based on their protein expression levels in cancerous and normal tissues. We confirmed BMI1's role in LIHC using Schistosoma japonicum-infected mouse models and molecular experiments.ResultsWe identified a series of DEGs that mediate schistosomiasis, the parasitic disease caused by Schistosoma japonicum infection, and hepatocarcinogenesis, and constructed a suitable prognostic model. We analyzed the mechanisms by which these DEGs regulate disease and present the differences in prognosis between the different genotypes. Finally, we verified our findings using molecular biology experiments.ConclusionBioinformatics and molecular biology analyses confirmed a relationship between schistosomiasis and liver hepatocellular cancer. Furthermore, we validated the role of a potential oncoprotein factor that may be associated with infection and carcinogenesis. These findings enhance our understanding of Schistosoma japonicum infection's role in LIHC carcinogenesis.

Entropy Model of Rosin Autonomous Boolean Network Digital True Random Number Generator

A True Random Number Generator (TRNG) is an important component in cryptographic algorithms and protocols. The Rosin Autonomous Boolean Network (ABN) digital TRNG has been widely studied due to its nice properties, such as low energy consumption, high speed, strong platform portability, and strong randomness. However, there is still a lack of suitable entropy models to deduce the requirement of design parameters to ensure true randomness. The current model to evaluate the entropy of oscillator-based TRNGs is not applicable for Rosin ABN TRNGs due to low-frequency noise. This work presents a new, suitable stochastic model to evaluate the entropy of Rosin ABN TRNGs. Theoretical analysis and simulation experiments verify the correctness and the effectiveness of the model, and, finally, the appropriate sampling parameters for Rosin ABN TRNGs are given for sufficient entropy per random bit to ensure true randomness.

The Adhesive Effect of Corporate Spirituality on Organizational Internal-External Strategies and Sustainability

This particular study investigates the adhesive effect of corporate spirituality on Organizational internal-external strategies and its overall influence on Organizational sustainability. The study is governed and examined through the lens of organizational learning and also spirituality leadership theory. The research stems from the context of oil and gas firms from a developing Country (Nigeria) and it takes a quantitative approach, employing 30 pilot survey data from the research setting. The results found that corporate spirituality has a significant positive effect and it strengthens and moderates the relationship between Organizational internal-external strategies and the sustainability of Oil and Gas firms. Yet, the reality remains that even in leading Organizations that recognize the importance (directly or indirectly) of corporate spirituality and purpose, the concept seems to be slightly neglected and underappreciated. This study stands to enhance (with empirical evidence) the examined relationship, especially in Oil and Gas firms, which is a sector going through a turbulent phase globally in terms of sustainability. Ultimately, the study promotes corporate spirituality values among Organizations, which will subsequently result in the development of a suitable and resilient model towards sustainable performance.

Ensemble forecasting of Persian leopard (Panthera pardus saxicolor) distribution and habitat suitability in south-western Iran

Context Habitat loss and fragmentation are major threats to global biodiversity, especially for wide-ranging apex carnivores. The Persian leopard (Panthera pardus saxicolor) is an endangered species. Although populations of Persian leopards are declining, the species remains widely distributed across large areas of Iran. Aims This study aimed to determine habitat suitability for Persian leopards in the Khaeiz and Sorkh protected area of south-western Iran and to identify the most significant factors influencing their habitat use/selection and spatial distribution. Methods We performed species distribution modelling in two stages: First, we ran the model with three abiotic predictors: slope; aspect; and distance from water resources. In the second stage, modelling was conducted using three ecological predictors: caracal distribution; wild goat distribution; and livestock distribution. Ensemble modelling was applied based on five replicates of eight SDMs (species distribution models; GLM, CTA, FDA, GBM, ANN, MARS, RF and MaxEnt). Key results We observed only minor differences in habitat suitability between the abiotic and ecological models. Habitat suitability for Persian leopards was higher in steeper areas, close to water resources and near the distribution of caracals, livestock and wild goats. The ecological model predicted 2.03% (329 ha) more suitable habitat than the abiotic model did. Conclusions Most habitat suitability models focus on abiotic variables, but we found that ecological variables offer similar predictive power for determining the habitat suitability of Persian leopards. Implications Habitat suitability models for Persian leopards can be used to guide conservation and management decisions. They are also useful indicating where conflicts between predators and humans may occur.

FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records

With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT classification using connection records (FLM-ICR) to address privacy concerns and poor computational performance in analyzing users' private data in IoV. FLM-ICR, in the horizontally federated learning client-server architecture, utilizes an improved multi-layer perceptron and logistic regression network as the model backbone, employs the federated momentum gradient algorithm as the local model training optimizer, and uses the federated Gaussian differential privacy algorithm to protect the security of the computation process. The experiment evaluates the model's classification performance using the confusion matrix, explores the impact of client collaboration on model performance, demonstrates the model's suitability for imbalanced data distribution, and confirms the effectiveness of federated learning for model training. FLM-ICR achieves the accuracy, precision, recall, specificity, and F1 score of 0.795, 0.735, 0.835, 0.75, and 0.782, respectively, outperforming existing research methods and balancing classification performance and privacy security, making it suitable for IoV computation and analysis of private data.

Predictive modeling of Salmonella spp. growth behavior in cooked and raw chicken samples: Real-time PCR quantification approach and model assessment in different handling scenarios.

The increasing prevalence of Salmonella contamination in poultry meat emphasizes the importance of suitable predictive microbiological models for estimating Salmonella growth behavior. This study was conducted to evaluate the potential of chicken juice as a model system to predict the behavior of Salmonella spp. in cooked and raw chicken products and to assess its ability to predict cross-contamination scenarios. A cocktail of four Salmonella serovars was inoculated into chicken juice, sliced chicken, ground chicken, and chicken patties, with subsequent incubation at 10, 15, 20, and 25°C for 39h. The number of Salmonella spp. in each sample was determined using real-time polymerase chain reaction. Growth curves were fitted into the primary Baranyi and Roberts model to obtain growth parameters. Interactions between temperature and growth parameters were described using the secondary Ratkowsky's square root model. The predictive results generated by the chicken juice model were compared with those obtained from other chicken meat models. Furthermore, the parameters of the chicken juice model were used to predict Salmonella spp. numbers in six worst-case cross-contamination scenarios. Performance of the chicken juice model was evaluated using the acceptable prediction zone from -1.0 (fail-safe) to 0.5 (fail-dangerous) log. Chicken juice model accurately predicted all observed data points within the acceptable range, with the distribution of residuals being wider near the fail-safe zone (75%) than near the fail-dangerous zone (25%). This study offers valuable insights into a novel approach for modeling Salmonella growth in chicken meat products, with implications for food safety through the development of strategic interventions. PRACTICAL APPLICATION: The findings of this study have important implications in the food industry, as chicken juice could be a useful tool for predicting Salmonella behavior in different chicken products and thus reducing the risk of foodborne illnesses through the development of strategic interventions. However, it is important to recognize that some modifications to the chicken juice model will be necessary to accurately mimic all real-life conditions, as multiple factors particularly those related to food processing can vary between different products.

An innovative machine learning workflow to research China’s systemic financial crisis with SHAP value and Shapley regression

This study proposed a cutting-edge, multistep workflow and upgraded it by addressing its flaw of not considering how to determine the index system objectively. It then used the updated workflow to identify the probability of China’s systemic financial crisis and analyzed the impact of macroeconomic indicators on the crisis. The final workflow comprises four steps: selecting rational indicators, modeling using supervised learning, decomposing the model’s internal function, and conducting the non-linear, non-parametric statistical inference, with advantages of objective index selection, accurate prediction, and high model transparency. In addition, since China’s international influence is progressively increasing, and the report of the 19th National Congress of the Communist Party of China has demonstrated that China is facing severe risk control challenges and stressed that the government should ensure that no systemic risks would emerge, this study selected China’s systemic financial crisis as an example. Specifically, one global trade factor and 11 country-level macroeconomic indicators were selected to conduct the machine learning models. The prediction models captured six risk-rising periods in China’s financial system from 1990 to 2020, which is consistent with reality. The interpretation techniques show the non-linearities of risk drivers, expressed as threshold and interval effects. Furthermore, Shapley regression validates the alignment of the indicators. The final workflow is suitable for categorical and regression analyses in several areas. These methods can also be used independently or in combination, depending on the research requirements. Researchers can switch to other suitable shallow machine learning models or deep neural networks for modeling. The results regarding crises could provide specific references for bank regulators and policymakers to develop critical measures to maintain macroeconomic and financial stability.

Evaluation and Re-Estimation of Bankruptcy Prediction Models in Facing The Crisis Period in Indonesia (2017-2022)

This research was conducted to see how suitable the existing bankruptcy prediction models that have been used in other countries to be used during the crisis in Indonesia. The data used in research are companies in Indonesia registered in the Indonesia Stock Exchange (IDX). The re-estimate of the coefficient of variables models is carried out and then the bankruptcy prediction of the re-estimation model is re-calculated. The results of the bankruptcy prediction of the re-estimate model are then compared with the results of the bankruptcy prediction of the original model to see whether the model can be used during the crisis in Indonesia. The results of the study is that Springate original model is the most suitable model for the conditions in Indonesia during the crisis caused by the COVID-19 pandemic. The Springate model has the highest financial distress prediction accuracy, while the Altman Emerging Market model produces the highest Error Type I.

Construction of Mesenchymal Stem Cell-Derived Artificial Human Urinary Bladder: A Preliminary Study

Objective: The present study aimed to obtain the required cells and select a suitable scaffold material for constructing an artificial bladder using the tissue engineering approach. 
 Materials and methods: The convenience of obtaining human adipose tissue-derived stem cells (hADMSCs) was used in this study. It was attempted to differentiate these cells into smooth muscle cells (SMC), which are present along the wall of the bladder. Urothelial cells were enzymatically isolated from tissue biopsies. Synthetic (poly-lactide co-glycolic acid, PLGA) and natural (chitosan) polymers were used in scaffold fabrication using a tissue engineering approach.
 Results: In the cellular experiments, urothelial cells couldn’t be cultured in polystyrene culture vessels in vitro and required a support material to maintain viability. Better results were obtained with the feeder layer. The hADMSCs exhibited the expected morphological changes in the serum-rich medium content in the SMC differentiation experiments. Chitosan, biocompatible and biodegradable, was mixed with PLGA as an alternative scaffold combination.
 Conclusion: This study indicated that hADMSCs-derived smooth muscle cells and biopsy-isolated urothelial cells cultured on hybrid chitosan–PLGA scaffolds with appropriate physical properties could serve as a suitable model for tissue-engineered artificial bladder construction.

Diethylhexyl phthalate exposure amplifies oxidant and inflammatory response in fetal hyperglycemia model predisposing insulin resistance in zebrafish embryos.

Exposure of zebrafish embryos to glucose is a suitable model for the fetal hyperglycemia seen in gestational diabetes. Diethylhexyl phthalate (DEHP), which is considered an endocrine-disrupting chemical, is one of the most common phthalate derivatives used in stretching plastic and is encountered in every area where plastic is used in daily life. In the present study, the effects of DEHP on pathways related to insulin resistance and obesity were examined in zebrafish embryos exposed to glucose as a fetal hyperglycemia model. Zebrafish embryos were exposed to DEHP, glucose, and glucose + DEHP for 72 h post-fertilization (hpf), and developmental parameters and locomotor activities were monitored. At 72 hpf ins, lepa, pparγ, atf4a, and il-6 expressions were determined by RT-PCR. Glucose, lipid peroxidation (LPO), nitric oxide (NO) levels, glutathione S-transferase (GST), superoxide dismutase (SOD), and acetylcholine esterase (AChE) activities were measured spectrophotometrically. Compared with the control group, glucose, LPO, GST activity, il6, and atf4a expressions increased in all exposure groups, while body length, locomotor, and SOD activities decreased. While AChE activity decreased in the DEHP and glucose groups, it increased in the glucose + DEHP group. Although glucose exposure increased pparγ and lepa expressions, DEHP significantly decreased the expressions of pparγ and lepa both in the DEHP and glucose + DEHP groups. Our findings showed that DEHP amplified oxidant and inflammatory responses in this fetal hyperglycemia model, predisposing insulin resistance in zebrafish embryos.

Simulation system design for critical-node detection and robustness analysis of supply chains

ABSTRACT A well-developed manufacturer requires a stable supply chain network that is robust to crises. In this study, a simulation system was developed to build a specialised supply chain model and analyse the critical nodes and risks. It makes three major theoretical contributions to literature. First, a novel supply chain model and cascading failure model are developed with adjustable power-law degree distributions for companies to identify suitable models. Second, a novel approach to the critical-node detection problem, considering cascading failure, is proposed. Finally, based on the proposed algorithm, supply chain vulnerability is analysed under two types of attacks.

Drowning in the Information Flood: Machine-Learning-Based Relevance Classification of Flood-Related Tweets for Disaster Management

In the early stages of a disaster caused by a natural hazard (e.g., flood), the amount of available and useful information is low. To fill this informational gap, emergency responders are increasingly using data from geo-social media to gain insights from eyewitnesses to build a better understanding of the situation and design effective responses. However, filtering relevant content for this purpose poses a challenge. This work thus presents a comparison of different machine learning models (Naïve Bayes, Random Forest, Support Vector Machine, Convolutional Neural Networks, BERT) for semantic relevance classification of flood-related, German-language Tweets. For this, we relied on a four-category training data set created with the help of experts from human aid organisations. We identified fine-tuned BERT as the most suitable model, averaging a precision of 71% with most of the misclassifications occurring across similar classes. We thus demonstrate that our methodology helps in identifying relevant information for more efficient disaster management.

Adopting a reductionist approach to advance acoustic deterrents in fish conservation

IntroductionDeterrents that use acoustics to guide fish away from dangerous areas (e.g., water intakes) depend on the elicitation of avoidance in the target species. Background noise is often neglected when testing acoustic deterrents, but it is important to account for its effects as freshwater environments present a wide variety of ambient soundscapes.MethodsUsing the widely studied goldfish (Carassius auratus) as a suitable experimental model, this study adopted a reductionist approach to investigate the relationship between the startle response to a pure tone signal and background noise. Under laboratory conditions, the startle responses of individual goldfish exposed to 120 ms tones at 250 Hz and four Sound Pressure Levels (SPLs: 115, 125, 135, 145 dB re 1 μPa) were quantified in the presence (treatment) and absence (ambient - control) of band-limited random noise (105 dB re 1 μPa).Results and discussionWhen observing the dose-response relationship, the proportion of fish that startled to the signal increased with SPL in both the treatment and control, although there was no difference between them, suggesting that the signal-to-noise ratio was not influential under the conditions tested. However, further analysis using Signal Detection Theory indicated that the was higher in the noisy treatment than the control when responding to both false alarms (startle during a pre-signal period) and hits (startle to an external stimulus during the pre-signal period). Furthermore, fish were better able to discriminate (d?) external stimuli over time (during the pre-signal period) in the treatment than control. There is a need to consider the role of background noise when designing acoustic fish deterrents that depend on the exhibition of avoidance behaviors.

GIS-MCE based suitability analysis for sustainable estate development in Ede North LGA Osun State, Nigeria

Ideal areas for housing developments that are, for the most part, sustainable present critical issues in the planning and development of eco-friendly urban settlements. With this in mind and explicitly, Ede North LGA of Osun State was chosen as the case study, where a methodological framework for a land suitability analysis was adopted to identify suitable lands for real estate development with sustainability as a focus in the realization of global UN goals. This present study falls within the framework of the Multi-Criteria Evaluation (MCE) analysis, which can be integrated into Geographic Information Systems (GIS) together with the Analytic Hierarchical Process (AHP), useful for decision-making in site selection studies. For this purpose, computed relative weights from the pairwise comparisons (AHP) of 5 sub-models, i.e., terrain, environmental, socio-economic, available land, and soil factors, were inserted into the suitability analysis function of GIS and combined in a ‘Weighted Sum’ overlay operation to produce a final suitability map on a scale of 1–5, with 5 being the highest preference. Lastly, the ‘Locate Region’ Spatial Analyst tool was applied to the final suitability map to locate three (3) optimum locations to site the estate developments. Conclusively, in view of the analysis and discoveries made in this research, finding optimum locations via the GIS-MCE-based land suitability model for future housing development needs may prove highly resourceful. Hence, this study can likewise give a significant direction to future land use changes and practical and cost-effective arrangements in urban areas where conditions are similar to those in Ede North LGA.

Going back for the future: Incorporating Pleistocene fossil records of saiga antelope into habitat suitability models

AbstractAimMany species have suffered anthropogenic range contraction and no longer occupy all available suitable environmental conditions. This is particularly problematic for the construction of habitat suitability models (HSMs), which assume that a species' contemporary range reflects its full species–environment relationship. HSMs therefore risk underestimating suitable environment areas, and misinforming conservation decisions. Incorporating historic (centuries‐old) records partly reduces this bias, but even these records are also subject to human disturbance. We incorporated fossil records of the critically endangered saiga antelope (Saiga tatarica, L., 1776), alongside historic and current records, into current and future habitat suitability models. Saiga has experienced drastic range contraction and may have a truncated species–environment relationship. The results allowed us to test whether its current habitat provides optimal environmental conditions, or whether saiga should be considered a refugee species.LocationNorthern Hemisphere.TaxonSaiga tatarica (Bovidae, Artiodactyla).MethodsWe collated historic and fossil saiga occurrence records from published literature, museum archives and global databases. Modern occurrence records were obtained from the International Union for Conservation of Nature Red List assessment. Four bioclimatic variables were downloaded from Worldclim.org. HSMs were generated through Maxent, using the maxnet package in R. Three HSMs were developed: present only, present historic and present fossil. Each of these models was projected onto current and two future (2070) climate change scenarios.ResultsSaiga fossil records increased the predicted suitable environment area by 783% and 1416% for current and future climate projections respectively. Our results suggest the saiga is not a refugee species but occupies only a portion of its potential environmental niche. The saiga's contemporary range is predicted environmentally suitable throughout all models and projections, and therefore in situ conservation management is recommended.Main ConclusionsThis study highlights the importance of incorporating fossil records into HSMs to better understand species–environment relationships and develop more robust conservation strategies for appropriate endangered species.

Electronic structures and quantum capacitance of twisted mixed-dimensional van der Waals heterostructures of graphene/C2H based on tight-binding model

Constructing twisted mixed dimensional graphene-based van der Waals heterostructure (vdWH) is an effective strategy to manipulate the electronic structures and improve the quantum capacitance (C q) of graphene. In this work, mixed dimensional vdWH of graphene/C2H has been proposed owing to similar Dirac semimetal character of one-dimensional C2H with that of graphene. Meanwhile, the influence of twisting angle (θ) and interlayer interaction strength on the electronic structures and the C q of the MD vdWH are systemically explored based on tight binding model. With the fitted hopping integral parameters, it is found that the linear dispersion of the graphene is basically preserved but the bandwidth is decreased with modulating twisting angle and interlayer interaction, and the C q of mixed dimensional vdWH is improved 5–19 times compared with graphene at zero bias. Moreover, the compressed strain could enhance the C q of mixed dimensional vdWH to 74.57 μF cm−2 at zero bias and broaden the low working voltage window of mixed-dimensional vdWH with considerable C q. Our results provide suitable tight-binding model parameters and theoretical guidance for exploring the twisted MD vdWH of graphene/C2H and offer an effective strategy to modulate the electronic structures and the C q of graphene through constructing the MD vdWH.

Cerebral Perfusion Characteristics and Dynamic Brain Structural Changes in Stroke-Prone Renovascular Hypertensive Rats: A Preclinical Model for Cerebral Small Vessel Disease.

Hypertension is a leading cause of cerebral small vessel disease (CSVD) and vascular dementia in elderly individuals. We aimed to assess cerebral perfusion and dynamic changes in brain structure in stroke-prone renovascular hypertensive rats (RHRSPs) with different durations of hypertension and to investigate whether they have pathophysiological features similar to those of humans with CSVD.The RHRSP model was established using the two-kidney, two-clip (2k2c) method, and the Morris water maze (MWM) test, MRI, immunohistochemistry, and biochemical analysis were performed at multiple time points for up to six months following the 2k2c operation.Systolic blood pressure was significantly greater in the RHRSP group than in the sham-operated group at week 4 post-surgery and continued to increase over time, leading to cognitive decline by week 20. Arterial spin labeling revealed cerebral hypoperfusion in the RHRSP group at 8 weeks, accompanied by vascular remodeling and decreased vessel density. Diffusion tensor imaging and Luxol fast blue staining indicated that white matter disintegration and demyelination gradually progressed in the corpus callosum and that myelin basic protein levels decreased. Eight weeks after surgery, blood-brain barrier (BBB) leakage into the corpus callosum was observed. The albumin leakage area was negatively correlated with the myelin sheath area (r=-0.88, p<0.001). RNA-seq analysis revealed downregulation of most angiogenic genes and upregulation of antiangiogenic genes in the corpus callosum of RHRSPs 24 weeks after surgery.RHRSPs developed cerebral hypoperfusion, BBB disruption, spontaneous white matter damage, and cognitive impairment as the duration of hypertension increased. RHRSPs share behavioral and neuropathological characteristics with CSVD patients, making them suitable animal models for preclinical trials related to CSVD.

Structure and chronology of a star dune at Erg Chebbi, Morocco, reveals why star dunes are rarely recognised in the rock record

Star dunes are the tallest dunes on Earth and are amongst the larger and more spectacular aeolian landforms. Although they are widespread in modern sandy deserts, star dunes are rarely recognised in the rock record probably due to a lack of suitable sedimentary models. This paper presents a new sedimentary model for the structure of a star dune at Erg Chebbi in Morocco (Sahara Desert) on the basis of ground-penetrating radar (GPR) surveys. Individual sedimentary structures in star dunes are similar to those in linear or barchanoid dunes, likely leading to misidentification in the rock record. However, the suite of features described in this paper will permit identification of star dunes in future studies of the rock record. Optically stimulated luminescence (OSL) dating shows that accumulation of the Erg Chebbi star dune post-dates the end of the African Humid Period (AHP). At the base of the dune, there is an ~ 8000-year hiatus in the record. Since then, the dune has grown rapidly to create a 100 m high dune within the past 1000 years and is migrating towards the west. Changes in the cross-strata support the idea that star dune construction was accompanied by a change in the wind directions.