Neighbor Analysis Research Articles

LGG-13. UNRAVELING THE SPATIAL TUMOR MICROENVIRONMENT OF PEDIATRIC LOW-GRADE GLIOMAS USING IMAGING MASS CYTOMETRY

Abstract Pediatric low-grade gliomas (pLGG) are the most common brain tumors in children and are associated with significant morbidity. Therefore, there is an urgent need for novel therapeutic strategies. While some studies have described pLGG’s tumor microenvironment (TME) from bulk RNAseq and scRNAseq, little is known about the spatial architecture of pLGG and its association with clinico-molecular features. Our study utilized imaging mass cytometry and a panel of 35 metal-labelled antibodies to unravel the spatial organization of key TME cell populations in 120 primary pLGG samples from the LOGGIC Core BioClinical DataBank. Cellular neighborhood analysis was performed to map the spatial organization of pLGG TME. Several clinic-molecular features (entity, tumor location, genetic driver alteration, disease progression status, age and sex) were used to measure their putative association with the enrichment of key cell populations and cellular structures to identify diagnosis and prognosis markers. Here, we identified a predominant presence of myeloid cells in the TME, particularly notable in optic pathway tumors, which exhibited unique immune profiles. Optic pathway tumors demonstrated elevated immune subsets, delineating distinct architectural features in the TME of this brain region. Spatial analysis defined cellular neighborhoods and specific interactions thereof, including myeloid interaction and macrophage-abundant regions. Clinically, these myeloid cell populations, associated with an increased expression of the immune checkpoint protein TIM3, suggesting the presence of an immuno-suppressive environment, were associated with inferior survival outcomes. Importantly, we identified an immunophenotype signature based on the presence of 4 myeloid cell populations significantly associated with progression free survival in our cohort. Our study underscored the need for accurate identification of immune cell populations influencing tumor progression, offering valuable insights for the identification of prognostic markers, and for the development of effective therapeutic strategies, such as immune checkpoint inhibitors, for the treatment of pLGG.

The long-run implications of slum clearance: A neighborhood analysis

This paper analyzes the federal urban renewal and slum clearance program. This program was one of the largest and most controversial policies used to rehabilitate neighborhoods in the United States. Using a newly constructed dataset, I examine the characteristics of neighborhoods cleared for redevelopment and the effect that such projects had on neighborhoods over time. I show that conditional on experiencing urban blight, Black neighborhoods were twice as likely as white neighborhoods to be targeted for clearance. Redevelopment led to a decline in housing density, population density, and the share of Black residents while simultaneously increasing median rents and incomes.

Atomistic simulation of temperature dependence of tensile properties and estimation of DBTT of Cu–Ag core–shell nanowires

An atomistic model of Cu-Ag core–shell nanowire using molecular dynamics is proposed. The simulation model was utilized for the evolution of temperature dependent (in the range of 10–500 K) tensile behavior and the identification of ductile-to-brittle transition temperature (DBTT) of single-crystal Cu-Ag core–shell nanowire. The evolution of defects such as point defects, dislocations, and stacking faults, arising from tensile deformation in the mentioned temperature rage were analyzed. The Wigner-Seitz Defect analysis, Dislocation analysis, and Common Neighbor analysis methods were implemented in this study to estimate the various defects concentration in the nanowire. The study identified that Cu-Ag core–shell (core diameter = 4 nm and shell thickness = 1 nm nanowire) exhibits DBTT somewhere in the temperature between 25 K to 30 K. The modulus of elasticity found to be increasing from 19.7 TPa at 10 K to 23.7 TPa at 30 K (within brittle regime), followed by a steep fall to 14.9 TPa at 500 K where fracture seems to be in ductile mode. The observed mechanical properties of the nanowire are discussed in the article in view of the dislocation and stacking faults generation in the core–shell nanostructures.

SAR target recognition through adaptive kernel sparse representation model based on local contrast perception

Sparse representation-based methods have shown promising prospects in synthetic aperture radar (SAR) target recognition due to their robustness under complex application conditions. However, these methods are still susceptible to the feature representation of SAR images. In this paper, we propose a new SAR target recognition method using an adaptive kernel sparse representation model based on local contrast perception (LCP). The LCP method combines a specially designed random projection matrix with two-dimensional filtering to capture extensive intensity contrasts between local regions in SAR images. A response map reconstruction technique shows that LCP can obtain discriminative properties between target and background regions and improve feature representation. The LCP features are then transformed into the kernel representation to improve linear separability by defining a reproducing kernel Hilbert space (RKHS) and using a nonlinear kernel function. The kernel representation is utilized to construct a new kernel sparse representation model for classification. Furthermore, neighborhood analysis (NA) is proposed to establish the relationship between the parameter and feature distribution, with an adaptive regularization parameter selection strategy to mitigate the parameter sensitivity. Experiments conducted on the moving and stationary target acquisition and recognition dataset demonstrate the superiority and robustness of the proposed method under different application conditions.

Spatially mapping the single-cell immune landscapes of melanoma brain metastases using imaging mass cytometry.

e21517 Background: Up to 60% of metastatic melanoma patients develop brain metastases, and 5% develop leptomeningeal disease (LD), which presents with the worst prognosis of any melanoma brain metastasis (MBM) infiltration pattern. Median survival duration for MBM is ~12 months, however for LD patients it can be < 10 weeks. The spatial immune landscape of MBM with and without LD remains poorly understood. Methods: To spatially characterize the tumor microenvironment (TME) of MBM, we performed CyTOF Imaging Mass Cytometry (CyTOF-IMC) on a highly multiplexed panel of 35 antibodies for 21 MBMs (13 with LD, 8 without LD). We performed a novel cell segmentation, cell type assignment and identification approach to identify cell lineages to spatially characterize the TME of MBMs, segmenting and classifying 130,000 cells into 19 cell types. Results: We found enrichment of dendritic cells in the direct neighbourhood of melanoma cells of patients with LD (p = 0.019) and trends towards elevation of anti-tumoral monocyte populations in the TME of patients without LD. To investigate survival outcomes, we divided our cohort into long (≥365days) and short-survivors ( < 365 days), and found significant elevation of anti-tumoral T-cell populations (Total T-cells, CD8+ T-cells, CD4+ T-cells and CD3+CD4-CD8- T-cells, p < 0.05), and closer proximity of T-cells (CD8+ & CD4+ T-cells) to melanoma cells (p < 0.05). Survival analysis using median cell-proportion for each T-cell subset showed statistically significant (p < 0.05) survival differences between patients with high and low cell-proportions of CD8+ T-cells and CD4+ T-cells, with similar trends observed for a subset of patients (n = 13) with LD and a subset of patients treated with ICIs (n = 10), and results were confirmed by a Cox Regression Model. Cellular neighborhood (CN) analysis was performed generating N = 12 stable CNs. We found patients with higher proportions of CN9 (M1-like-microglia/M2-like-microglia/melanoma), CN10 (M1-like-macrophages/M2-like-macrophages/melanoma) and CN12 (Cytotoxic-T-cells/Helper-T-cells/melanoma) was associated with longer overall survival after diagnosis with MBM (p < 0.01). Conclusions: Proportion and proximity of anti-tumoral monocyte and T-cell populations may play a key role in modulating tumor response and overall survival for patients with MBM, including for patients with LD or for patients treated with ICIs.

Spatial analysis of mosque distribution for inclusive community services in Banda Aceh City

Abstract In urban development, social facilities, especially mosques, are crucial in residential areas. This study in Banda Aceh City, governed by Islamic Shari’a principles, explores the multifunctional role of its 110 mosques beyond worship. They serve as hubs for Islamic festivals, educational activities, and communal deliberations. The research aims to analyze the spatial distribution and accessibility of these mosques within residential areas. Using buffer and Nearest Neighbor Analysis, four mosque typologies are identified: 1 provincial mosque, 7 large mosques, 79 jami’ mosques, and 23 public mosques. The scattered distribution pattern, with a ratio of 1.1849, persists across sub-districts. Coverage analysis reveals that mosque services extend to 71.5% of the city, with the Baiturrahman sub-district achieving a 100% coverage. These findings offer crucial insights for urban planning and policy development, emphasizing the potential transformation of mosques into comprehensive community centers addressing diverse social needs beyond their religious functions. This research underscores the importance of strategically aligning mosque provision with local population needs and demographic factors, promoting a more inclusive and integrated urban landscape.

Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts.

Lithium-based disordered rocksalts (LDRs), which are an important class of positive electrode materials that can increase the energy density of current Li-ion batteries, represent a significantly complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput screening approaches. Notably, atom-centered machine-learned interatomic potentials (MLIPs) are a promising pathway to accurately model the potential energy surface of highly disordered chemical spaces, such as LDRs, where the performance of such MLIPs has not been rigorously explored yet. Here, we represent a comprehensive evaluation of the accuracy, transferability, and ease of training of five atom-centered MLIPs, including the artificial neural network potentials developed by the atomic energy network (AENET), the Gaussian approximation potential (GAP), the spectral neighbor analysis potential (SNAP) and its quadratic extension (qSNAP), and the moment tensor potential (MTP), in modeling a 11-component LDR chemical space. Specifically, we generate a DFT-calculated data set of 10,842 configurations of disordered LiTMO2 and TMO2 compositions, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and/or Cu. To provide a point-of-comparison on the performance of atom-centered MLIPs, we also trained the neural equivariant interatomic potential (NequIP) on a subset of our data. Importantly, we find AENET to be the best potential in terms of accuracy and transferability for energy predictions, while MTP is the best for atomic forces. While AENET is the fastest to train among the MLIPs considered at low number of epochs (300), the training time increases significantly as epochs increase (3300), with a corresponding reduction in training errors (∼60%). Note that AENET and GAP tend to overfit in small data sets, with the extent of overfitting reducing with larger data sets. Finally, we observe AENET to provide reasonable predictions of average Li-intercalation voltages in layered, single-TM LiTMO2 frameworks, compared to DFT (∼10% error on average). Our study should pave the way both for discovering novel disordered rocksalt electrodes and for modeling other configurationally complex systems, such as high-entropy ceramics and alloys.

Efficient Implementation of the Binary Common Neighbor Analysis for Platinum-Based Intermetallics

The common neighbor analysis (CNA) for binary systems is a powerful method used to identify chemical ordering in intermetallics by unique indices. The capability of binary CNA, however, is largely restricted by the availability of indices for various ordered phases. In this study, CNA indices of 11 ordered phases derived from a face-centered cubic structure were introduced on a case-by-case basis. These phases, common in intermetallics containing platinum-group metals, include C11b, MoPt2, C6, B11, AgZr, A2B2[111], A2B2[113], Pt3Tc, A3B[011], A3B[111], and A3B[113]. The chemical order in static chemical perturbation, dynamic phase competition, and experimentally reconstructed nanophase alloys were identified using binary CNA. The results indicated that the proposed version of binary CNA exhibited significantly higher accuracy and robustness compared to the short-range order, polyhedral template matching, and the original binary CNA method. Benchmarked against available methods, the formation, decomposition, and competition of specifically ordered phases in bulks and nanoalloys were well reflected by present CNA, highlighting its potential as a robust and widely adopted tool for deciphering chemical ordering at the atomic level.

Microstructural Evolution of Al under Computational Analysis of Uniaxial [100] Compression

The strain rate exerts a profound influence on the mechanical characteristics of nanomaterials. To investigate this phenomenon, the molecular dynamics approach was employed to examine the impact of uniaxial compression along the [100] crystallographic direction in monocrystalline Al. The purpose of this research was to determine the differences in reactions observed during the elastic and plastic phases. It employed the Embedded Atom Method (EAM) as well as the Modified Embedded Atom Method (MEAM) potentials at 300 K. A comparative analysis of the outcomes from these potentials demonstrated considerable disparities. The results encompassed the percentage distribution of crystal structures (fcc, hcp, bcc, and others) as well as their atomic configurations. Several analytical factors were examined, including the strain-stress curve, the radial distribution function (RDF), the common neighbor analysis (CAN). The applied MEAM potential represents a subsequent occurrence of transitions following EAM, encompassing both increasing and decreasing phase transitions.

Analisis Spasial Pengelolaan Sampah Rumah Tangga di Kota Bukittinggi

This study aims to determine the distribution pattern of landfills in the city of Bukittinggi and the route of waste transportation vehicles so that people know the time and route of waste disposal. This research uses a quantitative method used to calculate the value of the distribution of landfill areas in the city of Bukittinggi. The analysis technique used is to use nearest neighbor analysis with routes and tracking. After obtaining the route, data processing will be carried out using the ArcGis application so that a map of the garbage transportation distribution path is obtained. Based on the research that has been done, it is found that the distribution pattern of Bukittinggi TPS through Average Nearest Neighbor analysis using ArcMap application. 10.4.1 is random and more located close to the city center.

Programming-free Image-Analysis Workflows for IHC, IF and Spatial Proteomics: More than just Cell Counting

Quantitative analysis of cells using a specific protein marker is one of the most frequently performed tasks in both preclinical and clinical histopathology. The primary alternatives include immunohistochemistry and immunofluorescence. This article will briefly shed some light on the pros and cons of both methods, the main ones being that immunohistochemistry is widely available, but in many laboratories, it is limited to only one marker per image. Utilizing more than one marker can be achieved through methods such as establishing duplex or triplex stains, serial sections, or re-staining. However, immunofluorescence excels where a deep multi-marker characterization of single cells is required. Spatial proteomics systems have recently increased the plexity to as many as 100 parallel markers, allowing advanced co-expression, rare cells, and spatial neighbourhood analyses. Bioinformatic analysis aspects for both modalities are discussed, outlining two generic workflows as they are realised in a professional image analysis software used by biomedical researchers. While cell segmentation and typing are at the core, a number of pre- and post-processing steps, such as tissue detection, comparison of ROIs, hotspot search, spatial clustering or neighbourhood analysis should be performed to provide more comprehensive read-outs.

Proper orthogonal descriptors for multi-element chemical systems

We introduce the proper orthogonal descriptors for efficient and accurate interatomic potentials of multi-element chemical systems. The potential energy surface of a multi-element system is represented as a many-body expansion of parametrized potentials which are functions of atom positions, atom types, and parameters. The proper orthogonal decomposition is employed to decompose the parametrized potentials as a linear combination of orthogonal basis functions. The orthogonal basis functions are used to construct proper orthogonal descriptors based on the elements of atoms, thus leading to multi-element descriptors. We compose the multi-element proper orthogonal descriptors to develop linear and quadratic interatomic potentials. We devise an algorithm to efficiently compute the total energy and forces of the interatomic potentials constructed from the proper orthogonal descriptors. The potentials are demonstrated for indium phosphide and titanium dioxide in comparison with the spectral neighbor analysis potential (SNAP) and atomic cluster expansion (ACE) potentials.

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

MotivationLong non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.ResultsWe propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.AvailabilitySource code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git

Spatial Analysis of Response Initiative to the outbreak of Diphtheria in Ojo Local Government Area of Lagos State, Nigeria.

In the Africa Continent, diphtheria remains a pressing public health concern. Despite advancements in healthcare infrastructure and vaccination efforts, several countries continue to report cases and outbreaks, indicating ongoing transmission of the disease. This research aimed at utilizing GIS application in diphtheria outbreak in the Ojo Local Government Area (LGA) of Lagos State, Nigeria, with the following objectives: (i) obtain the spatial distribution pattern of the spread of diphtheria outbreak. (ii) determine the area requiring immediate attention and response in the study area. (iii) showcase areas that are vulnerable within the study area. In the pursuance of this both primary data, including geographical coordinates of confirmed diphtheria cases, and secondary data such as recorded cases, population density, vaccination coverage, and transportation networks, the research utilizes ArcGIS for visualization and analysis, Differential Global Positioning System (DGPS) receivers ensured accurate spatial data collection, supplemented by ground truthing of secondary data. The result revealed significant clusters of diphtheria cases in Akesan-Obadore and Ijegun, indicating a higher concentration of infections. The study applied average nearest neighbor analysis, which indicated a dispersed pattern of outbreaks within the LGA. Furthermore, a risk assessment using Analytic Hierarchical Process (AHP), in identifying the southern parts and Iba ward of Ojo LGA as areas with highest risk. The study recommends to the Lagos State Government to enhance vaccination efforts and improve sanitation infrastructure in these high-risk areas to mitigate the spread of diphtheria.

Theoretical investigation of (La4O6)n, (La2Ce2O7)n, and (Ce4O8)n nanoclusters (n = 10, 18): Temperature effects and O-vacancy formation.

We report a theoretical investigation of temperature, size, and composition effects on the structural, energetic, and electronic properties of the (La4O6)n, (La2Ce2O7)n, and (Ce4O8)n nanoclusters (NCs) for n = 10, 18. Furthermore, we investigated the single O vacancy formation energy as a function of the geometric location within the NC. Our calculations are based on the combination of force-field molecular dynamics (MD) simulations and density functional theory calculations. We identified a phase transition from disordered to ordered structures for all NCs via MD simulations and structural analysis, e.g., radius changes, radial distribution function, common neighbor analysis, etc. The transition is sharp for La36Ce36O126, La20Ce20O70, and Ce72O144 due to the crystalline domains in the core and less abrupt for Ce40O80, La40O60, and La72O108. As expected, radius changes are abrupt at the transition temperature, as are morphological differences between NCs located below and above the transition temperature. We found a strong dependence on the O vacancy formation energy (Evac) and its location within the NCs. For example, for La40O60, Evac decreases almost linearly as the distance from the geometric center increases; however, the same trend was not observed for Ce40O80, while there are large deviations from the linear trend for La20Ce20O70. Evac has smaller values for Ce40O80 and higher values for La40O60, that is, almost three times, while Evac has intermediate values for mixed oxides, as expected from weighted averages. Therefore, the mixture of one formula unit of La2O3 with two formula units of CeO2 has the effect of increasing the stability of CeO2 (binding energy), which increases the magnitude of the formation energy of the O vacancy.

Fungal communities shift with soybean cyst nematode abundance in soils

Plant parasitic nematodes pose a significant threat to food security. Management strategies for these pathogens are limited, and additional tools for their control are needed. Some fungi have shown promise for biocontrol, however, their success in the field has varied. In contrast, some fungal plant pathogens form synergistic associations with nematodes resulting in increased plant disease severity. However, how both groups of fungi change with different plant parasitic nematode abundances in the soil is underexplored. In this study, we used the soybean cyst nematode as a model to understand these changes. We sampled soil from 171 Ohio soybean fields in 2019 and 2021 and determined soybean cyst nematode abundance. We identified the fungi in the samples through amplicon sequencing of the 18S-ITS rDNA regions. Edaphoclimatic factors were used to classify samples into geographic regions to account for environmental differences between sampling locations. We hypothesized that fungal communities would be influenced by both region and soybean cyst nematode abundance. K-near neighbor analysis revealed that fungal communities follow regional patterns. We also found that soybean cyst nematode abundance was associated with changes in these communities regardless of the region. Two potential nematophagous fungi were found to be prevalent in Ohio through core community analysis, although they were enriched when soybean cyst nematode abundance was high. Lastly, differential network analysis showed that interactions among fungal community members change when soybean cyst nematode is present in the soil. Together these results suggest that this nematode significantly shifts the fungal community composition in field soils.

Local gun violence, mental health, and sleep: A neighborhood analysis in one hundred US Cities

RationaleCommunity gun violence significantly shapes public health and collective well-being. Understanding how gun violence is associated with community health outcomes like mental health and sleep is crucial for developing interventions to mitigate disparities exacerbated by violence exposure. ObjectiveThis study examines the associations between community gun violence , insufficient sleep, and poor mental health across neighborhoods in the United States. MethodsWe utilized a novel database covering nearly 16,000 neighborhoods in 100 US cities from 2014 through 2019. Correlated trait fixed-effects models were employed to conduct all analyses while considering various neighborhood covariates such as concentrated disadvantage, demographic composition, population density, and proximity to trauma centers. ResultsOur analysis revealed that greater gun violence is associated with both insufficient sleep and poor mental health in subsequent years. There is a reciprocal relationship between poor mental health and insufficient sleep, with each partially mediating the other's association with community gun violence. Notably, gun violence exhibits the strongest direct association with poor sleep rather than with poor mental health. We found a consistent reciprocal relationship between sleep and mental health at the community level. ConclusionsThe findings highlight a complex interplay between community violence, sleep, and mental health, underlining the importance of reducing community violence through numerous long-term interventions to address health disparities across the US.

Abstract PO2-25-05: Spatial multiplexing of protein biomarkers for immune cell profiling of the tumor microenvironment with ChipCytometry

Abstract Understanding the spatial distribution of key immune cell populations is critical in advancing our understanding of cancer and the development of novel therapeutics. Historically, the spatial analysis of the tumor microenvironment (TME) has been limited to relatively low-plex immunohistochemical (IHC) or immunofluorescent (IF) assays, which were inadequate for deep immune cell profiling of the TME. Here, we present the analysis of fresh-frozen human breast tissue using spatial multiplexing technology called ChipCytometry, which combines iterative immuno-fluorescent staining using open-source antibodies for quantitative measure of virtually unlimited protein biomarkers on the same sample and high dynamic range imaging to facilitate quantitative phenotyping with single-cell resolution. In addition to traditional pathology review of the high-plex dataset, subsequent image analysis can be done using open-source or commercial software for advanced spatial analyses, including cluster analysis, neighborhood analysis and advanced data visualization. The results show precise expression levels for each of the protein biomarkers in the assay for each individual cell within the sample while maintaining spatial information about each cell. Dozens of immune cell subtypes were identified and quantified based on protein expression profiles. Spatial analysis of the samples revealed quantifiable heterogeneity of immune cell infiltration within the tumor samples, demonstrating the utility of the ChipCytometry platform for in-depth immune profiling in clinical samples. The ChipCytometry platform enables simultaneous detection of multiple protein biomarkers on a single tissue section for deep immune cell profiling in the TME. Combined with the single-cell spatial information, such datasets provide an opportunity for the discovery of new complex multiplexed biomarker signatures to inform therapeutic development and personalized medicine. Citation Format: Benton Berigan. Spatial multiplexing of protein biomarkers for immune cell profiling of the tumor microenvironment with ChipCytometry [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-25-05.

HYPERTENSIVE NEPHROPATHY: TRANSCRIPTOMICS OF KIDNEY BIOPSIES PREDICTS LONG TERM OUTCOME AND IDENTIFIES THERAPEUTIC TARGETS

Objective: Hypertensive nephropathy (HN) represents a major cause of chronic kidney disease, but it is incompletely understood why some patients show disease progression. Our project aim is to identify potential markers of disease progression and novel therapeutic targets. Design and method: Adult patients (n=43; n=16 females, mean age 53 years) with biopsy-verified HN were categorized as ’early’ (estimated glomerular filtration rate (eGFR) >45 ml/min/1.73m2) or ’late’ disease (eGFR <45 ml/min/1.73m2) at the time of biopsy. Patients were further divided into “stable” (eGFR decline <3 ml/min/year) or “progressive” (eGFR decline >3 ml/min/year or start of renal replacement therapy) after median follow-up of 10 years (5-22). TruSeq Exome sequencing was executed after RNA extraction (miRNeasy FFPE kit, Qiagen) at Novogene, Cambridge, UK. Quality control and data analysis was performed using R Studio (v4.2.0) and QIAGEN Ingenuity Pathway Analysis. Results: We analyzed the following subgroups of HN patients: early stable (ES, n=11), early progressive (EP, n=11), late stable (LS, n=9) and late progressive (LP, n=12). Differentially expressed genes (DEG, fold change (FC) >1.5 and p-value < 0.05) were identified, with n=265 in ES vs. EP, and n=674 in LS vs. LP. Principal component analysis (PCA) showed separation of ES vs. EP and LS vs. LP, as depicted in Figure 1A and B. K-nearest neighbour (KNN) analysis of DEG identified a 6-gene classifier in LS vs. LP (19/21 samples correctly classified), while IER5L and CNTNAP5 were the top 2-gene classifier in ES vs. EP (20/21 samples). These classifiers, as well as other DEGs such as PER1, YB1, TIMP3, ADAMTS4, IGFBP5 and EGF could represent novel targets to inhibit disease progression. Differentially regulated pathways were associated with regulation of TP53 activity and circadian rhythm involving melatonin metabolism in ES vs. EP, and metabolic processes related to water-soluble vitamins, glutathione and sphingolipids in LS vs. LP. Conclusions: Transcriptomic profiling from diagnostic kidney biopsies with HN can distinguish future disease progression from non-progression and may identify novel therapeutic targets.

Mapping coexisting hotspots of multidimensional food market (in)accessibility and climate vulnerability

With the increasing likelihood of agricultural production failures under a warmer global climate, the importance of markets in providing access to nutrient-dense foods (NDFs) through trade is predicted to grow. However, regions with relatively poor access to markets and supporting infrastructures (e.g. roads and storage facilities) are potentially ill-equipped to deal with both short-term hydrometeorological hazards such as droughts and floods, and longer-term shifts in agricultural productivity. Despite the increasing focus upon markets within academic and policymaking circles, a regional-scale assessment of these potentially coexisting hotspots of vulnerability has not been conducted. We conduct a two-stage geospatial analysis integrating three publicly available datasets across the Indian states of Bihar, Chhattisgarh, Jharkhand, and Odisha. Combining the 2011 national census with the new PMGSY-GeoSadak database, we conduct nearest neighbour analysis to measure multidimensional market inaccessibility by: (i) distance from a settlement to its nearest village, town or city with a market, (ii) distance from a settlement to its nearest major road, and (iii) distance from a settlement to its subdistrict headquarters. We then correlate these measures with India’s only district-wise assessment of climate vulnerability to identify hotspots of market inaccessibility and climate hazards. We find that the three market access measures are spatially autocorrelated and positively interrelated at the settlement (n = 129 555) and district (n = 107) levels, meaning that settlements located further from their nearest market tend to experience poorer road connectivity and access to the subdistrict economic hub. Approximately 18.5-million people live in districts with relatively high climate vulnerability and relatively high and multidimensional market inaccessibility. Hotspots of coexisting vulnerabilities are also disproportionately populated by ‘Schedule Castes and Schedule Tribes’ (SC/ST) communities. The identification of coexisting hotspots has important implications for the development of equitable and resilient markets that bolster NDF access for climate vulnerable and nutritionally insecure populations.