Persistent transcriptional changes in cardiac adaptive immune cells following myocardial infarction: New evidence from the re-analysis of publicly available single cell and nuclei RNA-sequencing data sets.

INTRODUCTION: Chronic immunopathology contributes to the development of heart failure after a myocardial infarction. Both T and B cells of the adaptive immune system are present in the myocardium and have been suggested to be involved in post-MI immunopathology. METHODS: We analyzed the B and T cell populations isolated from previously published single cell RNA-sequencing data sets (PMID: 32130914, PMID: 35948637, PMID: 32971526 and PMID: 35926050), of the mouse and human heart, using differential expression analysis, functional enrichment analysis, gene regulatory inferences, and integration with autoimmune and cardiovascular GWAS. RESULTS: Already at baseline, mature effector B and T cells are present in the human and mouse heart, having increased activity in transcription factors maintaining tolerance (e.g. DEAF1, JDP2, SPI-B). Following MI, T cells upregulate pro-inflammatory transcript levels (e.g. Cd11, Gzmk, Prf1), while B cells upregulate activation markers (e.g. Il6, Il1rn, Ccl6) and collagen (e.g. Col5a2, Col4a1, Col1a2). Importantly, pro-inflammatory and fibrotic transcription factors (e.g. NFKB1, CREM, REL) remain active in T cells, while B cells maintain elevated activity in transcription factors related to immunoglobulin production (e.g. ERG, REL) in both mouse and human post-MI hearts. Notably, genes differentially expressed in post-MI T and B cells are associated with cardiovascular and autoimmune disease. CONCLUSION: These findings highlight the varied and time-dependent dynamic roles of post-MI T and B cells. They appear ready-to-go and are activated immediately after MI, thus participate in the acute wound healing response. However, they subsequently remain in a state of pro-inflammatory activation contributing to persistent immunopathology.

Exploring Hopf-bifurcations and endemic bubbles in a tuberculosis model with behavioral changes and treatment saturation.

To manage risks and minimize the transmission of contagious diseases, individuals may reduce their contact with each other and take other precautions as much as possible in their daily lives and workplaces. As a result, the transmission of the infection reduces due to the behavioral changes. These behavioral changes are incorporated into models by introducing saturation in disease incidence. In this article, we propose and analyze a tuberculosis model that incorporates saturated exogenous reinfection and treatment. The stability analysis of the model's steady states is rigorously examined. We observe that the disease-free equilibrium point and the endemic equilibrium point (EEP) are globally asymptotically stable if the basic reproduction number (R0) is less than 1 and greater than 1, respectively, only when exogenous reinfection is not present (p=0) and when treatment is available for all (ω=0). However, even when R0 is less than 1, tuberculosis may persist at a specific level in the presence of exogenous reinfection and treatment saturation, leading to a backward bifurcation in the system. The existence and direction of Hopf-bifurcations are also discussed. Furthermore, we numerically validate our analytical results using different parameter sets. In the numerical examples, we study Hopf-bifurcations for parameters such as ß, p, α, and ω. In one example, we observe that increasing ß leads to the loss of stability of the unique EEP through a forward Hopf-bifurcation. If ß is further increased, the unique EEP restores its stability, and the bifurcation diagram exhibits an interesting structure known as an endemic bubble. The existence of an endemic bubble for the saturation constant ω is also observed.

Crop type discrimination using Geo-Stat Endmember Extraction and machine learning algorithms.

The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique based on imaging spectroscopy that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871,87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time of a large area with high accuracy.

Integrative genomics reveals pathogenic mediator of valproate-induced neurodevelopmental disability.

Prenatal exposure to the anti-seizure medication sodium valproate (VPA) is associated with an increased risk of adverse postnatal neurodevelopmental outcomes, including lowered intellectual ability, autism spectrum disorder and attention-deficit hyperactivity disorder. In this study, we aimed to clarify the molecular mechanisms underpinning the neurodevelopmental consequences of gestational VPA exposure using integrative genomics. We assessed the effect of gestational VPA on foetal brain gene expression using a validated rat model of valproate teratogenicity that mimics the human scenario of chronic oral valproate treatment during pregnancy at doses that are therapeutically relevant to the treatment of epilepsy. Two different rat strains were studied-inbred Genetic Absence Epilepsy Rats from Strasbourg, a model of genetic generalized epilepsy, and inbred non-epileptic control rats. Female rats were fed standard chow or VPA mixed in standard chow for 2 weeks prior to conception and then mated with same-strain males. In the VPA-exposed rats maternal oral treatment was continued throughout pregnancy. Foetuses were extracted via C-section on gestational Day 21 (1 day prior to birth) and foetal brains were snap-frozen and genome-wide gene expression data generated. We found that gestational VPA exposure via chronic maternal oral dosing was associated with substantial drug-induced differential gene expression in the pup brains, including dysregulated splicing, and observed that this occurred in the absence of evidence for significant neuronal gain or loss. The functional consequences of VPA-induced gene expression were explored using pathway analysis and integration with genetic risk data for psychiatric disease and behavioural traits. The set of genes downregulated by VPA in the pup brains were significantly enriched for pathways related to neurodevelopment and synaptic function and significantly enriched for heritability to human intelligence, schizophrenia and bipolar disorder. Our results provide a mechanistic link between chronic foetal VPA exposure and neurodevelopmental disability mediated by VPA-induced transcriptional dysregulation.

Seeing from space makes sense: Novel earth observation variables accurately map species distributions over Himalaya.

Topical advances in earth observation have enabled spatially explicit mapping of species' fundamental niche limits that can be used for nature conservation and management applications. This study investigates the possibility of applying functional variables of ecosystem retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard sensor data to map the species distribution of two alpine treeline species, namely Betula utilis D.Don and Rhododendron campanulatum D.Don over the Himalayan biodiversity hotspot. In this study, we have developed forty-nine Novel Earth Observation Variables (NEOVs) from MODIS products, an asset to the present investigation. To determine the effectiveness and ecological significance of NEOVs combinations, we built and compared four different models, namely, a bioclimatic model (BCM) with bioclimatic predictor variables, a phenology model (PhenoM) with earth observation derived phenological predictor variables, a biophysical model (BiophyM) with earth observation derived biophysical predictor variables, and a hybrid model (HM) with a combination of selected predictor variables from BCM, PhenoM, and BiophyM. All models utilized topographical variables by default. Models that include NEOVs were competitive for focal species, and models without NEOVs had considerably poor model performance and explanatory strength. To ascertain the accurate predictions, we assessed the congruence of predictions by pairwise comparisons of their performance. Among the three machine learning algorithms tested (artificial neural networks, generalised boosting model, and maximum entropy), maximum entropy produced the most promising predictions for BCM, PhenoM, BiophyM, and HM. Area under curve (AUC) and true skill statistic (TSS) scores for the BCM, PhenoM, BiophyM, and HM models derived from maximum entropy were AUC ≥0.9 and TSS ≥0.6 for the focal species. The overall investigation revealed the competency of NEOVs in the accurate prediction of species' fundamental niches, but conventional bioclimatic variables were unable to achieve such a level of precision. A principal component analysis of environmental spaces disclosed that niches of focal species substantially overlapped each other. We demonstrate that the use of satellite onboard sensors' biotic and abiotic variables with species occurrence data can provide precision and resolution for species distribution mapping at a scale that is relevant ecologically and at the operational scale of most conservation and management actions.

Quantifying the landscape changes within and outside the Dachigam National Park, Kashmir Himalaya, India using observations and models.

Protected areas are the cornerstone of biodiversity and serve as a haven for biodiversity conservation. However, due to immense anthropic pressures and ongoing changes in climate, the protected reserves are under immense threat. Human interference through land system changes is a major precusor of fragmentation of landscapes resulting in the decline of Himalayan biodiversity. In this context, this research assessed land use land cover changes (LULCCs) and fragmentation within and outside the Dachigam National Park (DNP) using remote sensing data, GIS-based models and ground truth over the past 55 years (1965-2020). Landscape Fragmentation Tool (LFT) helped to compute edge effect, patchiness, perforation and core areas. The Land Change Modeller (LCM) of IDRISI TerrSet was used for simulating the future LULC for the years 2030, 2050, 2700 and 2100. The analysis of LULCCs showed that built-up and aquatic vegetation expanded by 326% and 174%, respectively in the vicinity of the DNP. The area under agriculture, scrub and pasture decreased primarily due to intensified land use activities. Within the DNP, the area under forest cover declined by 7%. A substantial decrease was observed in the core zone both within (39%) and outside (30%) the DNP indicative of fragmentation of natural habitats. LCM analysis projected 10% increase in the built-up extents besides forests, shrublands and pastures. This knowledge generated in this study shall form an important baseline for understanding and characterising the human-wildlife relationship, initiating long-term ecological research (LTER) on naturally vegetated and aquatic ecosystems (primarily Dal Lake) of the region.

Synergistic Evaluation of Passive Microwave and Optical/IR Data for Modelling Vegetation Transmissivity towards Improved Soil Moisture Retrieval.

Vegetation cover and soil surface roughness are vital parameters in the soil moisture retrieval algorithms. Due to the high sensitivity of passive microwave and optical observations to Vegetation Water Content (VWC), this study assesses the integration of these two types of data to approximate the effect of vegetation on passive microwave Brightness Temperature (BT) to obtain the vegetation transmissivity parameter. For this purpose, a newly introduced index named Passive microwave and Optical Vegetation Index (POVI) was developed to improve the representativeness of VWC and converted into vegetation transmissivity through linear and nonlinear modelling approaches. The modified vegetation transmissivity is then applied in the Simultaneous Land Parameters Retrieval Model (SLPRM), which is an error minimization method for better retrieval of BT. Afterwards, the Volumetric Soil Moisture (VSM), Land Surface Temperature (LST) as well as canopy temperature (TC) were retrieved through this method in a central region of Iran (300 × 130 km2) from November 2015 to August 2016. The algorithm validation returned promising results, with a 20% improvement in soil moisture retrieval.

Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases.

Parkinson's disease (PD), Parkinson's disease with dementia (PDD) and dementia with Lewy bodies (DLB) are three clinically, genetically and neuropathologically overlapping neurodegenerative diseases collectively known as the Lewy body diseases (LBDs). A variety of molecular mechanisms have been implicated in PD pathogenesis, but the mechanisms underlying PDD and DLB remain largely unknown, a knowledge gap that presents an impediment to the discovery of disease-modifying therapies. Transcriptomic profiling can contribute to addressing this gap, but remains limited in the LBDs. Here, we applied paired bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from 28 individuals, including healthy controls, PD, PDD and DLB cases (n = 7 per group), to transcriptomically profile the LBDs. Using this approach, we (i) found transcriptional alterations in multiple cell types across the LBDs; (ii) discovered evidence for widespread dysregulation of RNA splicing, particularly in PDD and DLB; (iii) identified potential splicing factors, with links to other dementia-related neurodegenerative diseases, coordinating this dysregulation; and (iv) identified transcriptomic commonalities and distinctions between the LBDs that inform understanding of the relationships between these three clinical disorders. Together, these findings have important implications for the design of RNA-targeted therapies for these diseases and highlight a potential molecular "window" of therapeutic opportunity between the initial onset of PD and subsequent development of Lewy body dementia.

Nonlinear dynamical behavior of an SEIR mathematical model: Effect of information and saturated treatment.

When a disease spreads in a population, individuals tend to change their behavior due to the presence of information about disease prevalence. Therefore, the infection rate is affected and incidence term in the model should be appropriately modified. In addition, a limitation of medical resources has its impact on the dynamics of the disease. In this work, we propose and analyze an Susceptible-Exposed-Infected-Recovered (SEIR) model, which accounts for the information-induced non-monotonic incidence function and saturated treatment function. The model analysis is carried out, and it is found that when R0 is below one, the disease may or may not die out due to the saturated treatment (i.e., a backward bifurcation may exist and cause multi-stability). Further, we note that in this case, disease eradication is possible if medical resources are available for all. When R0 exceeds one, there is a possibility of the existence of multiple endemic equilibria. These multiple equilibria give rise to rich and complex dynamics by showing various bifurcations and oscillations (via Hopf bifurcation). A global asymptotic stability of a unique endemic equilibrium (when it exists) is established under certain conditions. An impact of information is shown and also a sensitivity analysis of model parameters is performed. Various cases are considered numerically to provide the insight of model behavior mathematically and epidemiologically. We found that the model shows hysteresis. Our study underlines that a limitation of medical resources may cause bi(multi)-stability in the model system. Also, information plays a significant role and gives rise to a rich and complex dynamical behavior of the model.

Denoising AVIRIS-NG Data for Generation of New Chlorophyll Indices.

The availability of Airborne Visible and Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data has enormous possibilities for quantification of Leaf Chlorophyll Content (LCC). The present study used the AVIRIS-NG campaign site of Western India for generation and validation of new chlorophyll indices by denoising the AVIRIS-NG data. For validation, concurrent to AVIRIS-NG flight overpass, field samplings were performed. The acquired AVIRIS-NG was subjected to Spectral Angle Mapper (SAM) classifier for discriminating the crop types. Three smoothing techniques i.e., Fast-Fourier Transform (FFT), Mean and Savitzky-Golay filters were evaluated for their denoising capability. Raw and filtered data was used for developing new chlorophyll indices by optimizing AVIRIS-NG bands using VIs based on parametric regression algorithms. In total, 20 chlorophyll indices and corresponding 20 models were developed for mapping LCC in the area. SAM identified 17 crop types in the area, while FFT found to be the best for filtering. Performance of these models when checked based on Pearson correlation coefficient (r) and Centered Root Mean Square Difference (CRMSD), indicated that LCC-CCI10 based on normalized difference type index formed through Near Infrared band and blue band is the best estimator of LCC (rcal = 0.73, rval = 0.66, CRMSD = 4.97). The approach was also tested using AVIRIS-NG image of the year 2018, which also showed a promising correlation (r = 0.704, CRSMD = 8.98, Bias = -0.5) between modeled and field LCC.

Proof-of-concept that network pharmacology is effective to modify development of acquired temporal lobe epilepsy.

Epilepsy is a complex network phenomenon that, as yet, cannot be prevented or cured. We recently proposed network-based approaches to prevent epileptogenesis. For proof of concept we combined two drugs (levetiracetam and topiramate) for which in silico analysis of drug-protein interaction networks indicated a synergistic effect on a large functional network of epilepsy-relevant proteins. Using the intrahippocampal kainate mouse model of temporal lobe epilepsy, the drug combination was administered during the latent period before onset of spontaneous recurrent seizures (SRS). When SRS were periodically recorded by video-EEG monitoring after termination of treatment, a significant decrease in incidence and frequency of SRS was determined, indicating antiepileptogenic efficacy. Such efficacy was not observed following single drug treatment. Furthermore, a combination of levetiracetam and phenobarbital, for which in silico analysis of drug-protein interaction networks did not indicate any significant drug-drug interaction, was not effective to modify development of epilepsy. Surprisingly, the promising antiepileptogenic effect of the levetiracetam/topiramate combination was obtained in the absence of any significant neuroprotective or anti-inflammatory effects as indicated by multimodal brain imaging and histopathology. High throughput RNA-sequencing (RNA-seq) of the ipsilateral hippocampus of mice treated with the levetiracetam/topiramate combination showed that several genes that have been linked previously to epileptogenesis, were significantly differentially expressed, providing interesting entry points for future mechanistic studies. Overall, we have discovered a novel combination treatment with promise for prevention of epilepsy.

Heavy Metal Soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom.

Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field- and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations.

Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India.

Forests are the potential source for managing carbon sequestration, regulating climate variations and balancing universal carbon equilibrium between sources and sinks. Further, assessment of biomass, carbon stock, and its spatial distribution is prerequisite for monitoring the health of forest ecosystem. Moreover, vegetation field inventories are valuable source of data for estimating aboveground biomass (AGB), density, and the carbon stored in biomass of forest vegetation. In view of the importance of biomass, the present study makes an attempt to estimate temporal AGB of Tripura State, India, using Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), leaf area index (LAI) and the field inventory data through geospatial techniques. A model was developed for establishing the relationship between biomass, LAI, and NDVI in the selected study site. The study also aimed to improve method for quantifying and verifying inventory-based biomass stock estimation. The results demonstrate the correlation value obtained between LAI and NDVI were 0.87 and 0.53 for the years 2011 and 2014, respectively. The correlation value between estimated AGB with LAI were found as 0.66 and 0.69, while with NDVI, the values were obtained as 0.64 and 0.94 for the years 2011 and 2014, respectively. The regression model of measured biomass with MODIS NDVI and LAI was developed for the data obtained during the period 2011-2014. The developed model was used to estimate the spatial distribution of biomass and its relationship between LAI and NDVI. The R2 values obtained were 0.832 for estimated and the measured AGB during the training and 0.826 for the validation. The results indicate that the methodology adopted in this study can help in selecting best fit model for analyzing relationship between AGB and NDVI/LAI and for estimating biomass using allometric equation at various spatial scales. The developed output thematic map showed an average biomass distribution of 32-94 Mg ha-1. The highest biomass values (72-95 Mg ha -1) was confined to the dense region of the forest while the lowest biomass values (32-46 Mg ha-1) was identified in the outer regions of the study site.

Standardization procedure for plasma biomarker analysis in rat models of epileptogenesis: Focus on circulating microRNAs.

The World Health Organization estimates that globally 2.4 million people are diagnosed with epilepsy each year. In nearly 30% of these cases, epilepsy cannot be properly controlled by antiepileptic drugs. More importantly, treatments to prevent or modify epileptogenesis do not exist. Therefore, novel therapies are urgently needed. In this respect, it is important to identify which patients will develop epilepsy and which individually tailored treatment is needed. However, currently, we have no tools to identify the patients at risk, and diagnosis of epileptogenesis remains as a major unmet medical need, which relates to lack of diagnostic biomarkers for epileptogenesis. As the epileptogenic process in humans is typically slow, the use of animal models is justified to speed up biomarker discovery. We aim to summarize recommendations for molecular biomarker research and propose a standardized procedure for biomarker discovery in rat models of epileptogenesis. The potential of many phylogenetically conserved circulating noncoding small RNAs, including microRNAs (miRNAs), as biomarkers has been explored in various brain diseases, including epilepsy. Recent studies show the feasibility of detecting miRNAs in blood in both experimental models and human epilepsy. However, the analysis of circulating miRNAs in rodent models is challenging, which relates both to the lack of standardized sampling protocols and to analysis of miRNAs. We will discuss the issues critical for preclinical plasma biomarker discovery, such as documentation, blood and brain tissue sampling and collection, plasma separation and storage, RNA extraction, quality control, and RNA detection. We propose a protocol for standardization of procedures for discovery of circulating miRNA biomarkers in rat models of epileptogenesis. Ultimately, we hope that the preclinical standardization will facilitate clinical biomarker discovery for epileptogenesis in man.

Modeling the role of information and limited optimal treatment on disease prevalence.

Disease outbreaks induce behavioural changes in healthy individuals to avoid contracting infection. We first propose a compartmental model which accounts for the effect of individual's behavioural response due to information of the disease prevalence. It is assumed that the information is growing as a function of infective population density that saturates at higher density of infective population and depends on active educational and social programmes. Model analysis has been performed and the global stability of equilibrium points is established. Further, choosing the treatment (a pharmaceutical intervention) and the effect of information (a non-pharmaceutical intervention) as controls, an optimal control problem is formulated to minimize the cost and disease fatality. In the cost functional, the nonlinear effect of controls is accounted. Analytical characterization of optimal control paths is done with the help of Pontryagin's Maximum Principle. Numerical findings suggest that if only control via information is used, it is effective and economical for early phase of disease spread whereas treatment works well for long term control except for initial phase. Furthermore, we observe that the effect of information induced behavioural response plays a crucial role in the absence of pharmaceutical control. Moreover, comprehensive use of both the control interventions is more effective than any single applied control policy and it reduces the number of infective individuals and minimizes the economic cost generated from disease burden and applied controls. Thus, the combined effect of both the control policies is found more economical during the entire epidemic period whereas the implementation of a single policy is not found economically viable.

Integrating phosphoproteome and transcriptome reveals new determinants of macrophage multinucleation.

Macrophage multinucleation (MM) is essential for various biological processes such as osteoclast-mediated bone resorption and multinucleated giant cell-associated inflammatory reactions. Here we study the molecular pathways underlying multinucleation in the rat through an integrative approach combining MS-based quantitative phosphoproteomics (LC-MS/MS) and transcriptome (high-throughput RNA-sequencing) to identify new regulators of MM. We show that a strong metabolic shift toward HIF1-mediated glycolysis occurs at transcriptomic level during MM, together with modifications in phosphorylation of over 50 proteins including several ARF GTPase activators and polyphosphate inositol phosphatases. We use shortest-path analysis to link differential phosphorylation with the transcriptomic reprogramming of macrophages and identify LRRFIP1, SMARCA4, and DNMT1 as novel regulators of MM. We experimentally validate these predictions by showing that knock-down of these latter reduce macrophage multinucleation. These results provide a new framework for the combined analysis of transcriptional and post-translational changes during macrophage multinucleation, prioritizing essential genes, and revealing the sequential events leading to the multinucleation of macrophages.

Genetic analysis of the cardiac methylome at single nucleotide resolution in a model of human cardiovascular disease.

Epigenetic marks such as cytosine methylation are important determinants of cellular and whole-body phenotypes. However, the extent of, and reasons for inter-individual differences in cytosine methylation, and their association with phenotypic variation are poorly characterised. Here we present the first genome-wide study of cytosine methylation at single-nucleotide resolution in an animal model of human disease. We used whole-genome bisulfite sequencing in the spontaneously hypertensive rat (SHR), a model of cardiovascular disease, and the Brown Norway (BN) control strain, to define the genetic architecture of cytosine methylation in the mammalian heart and to test for association between methylation and pathophysiological phenotypes. Analysis of 10.6 million CpG dinucleotides identified 77,088 CpGs that were differentially methylated between the strains. In F1 hybrids we found 38,152 CpGs showing allele-specific methylation and 145 regions with parent-of-origin effects on methylation. Cis-linkage explained almost 60% of inter-strain variation in methylation at a subset of loci tested for linkage in a panel of recombinant inbred (RI) strains. Methylation analysis in isolated cardiomyocytes showed that in the majority of cases methylation differences in cardiomyocytes and non-cardiomyocytes were strain-dependent, confirming a strong genetic component for cytosine methylation. We observed preferential nucleotide usage associated with increased and decreased methylation that is remarkably conserved across species, suggesting a common mechanism for germline control of inter-individual variation in CpG methylation. In the RI strain panel, we found significant correlation of CpG methylation and levels of serum chromogranin B (CgB), a proposed biomarker of heart failure, which is evidence for a link between germline DNA sequence variation, CpG methylation differences and pathophysiological phenotypes in the SHR strain. Together, these results will stimulate further investigation of the molecular basis of locally regulated variation in CpG methylation and provide a starting point for understanding the relationship between the genetic control of CpG methylation and disease phenotypes.

Modeling groundwater quality over a humid subtropical region using numerical indices, earth observation datasets, and X-ray diffraction technique: a case study of Allahabad district, India.

Water is undoubtedly the vital commodity for all living creatures and required for well-being of the human society. The present work is based on the surveys and chemical analyses performed on the collected groundwater samples in a part of the Ganga basin in order to understand the sources and evolution of the water quality in the region. The two standard indices such as water quality index and synthetic pollution index for the classification of water in the region are computed. The soil and sediment analysis are carried out with the help of X-ray diffractometer (XRD) for the identification of possible source of ions in water from rock and soil weathering. The dominant minerals which include quartz, muscovite, plagioclase, and orthoclase are reported in the area. The study further utilizes the multivariate statistical techniques for handling large and complex datasets in order to get better information about the groundwater quality. The following statistical methods such as cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA) are applied to handle the large datasets and to understand the latent structure of the data. Through FA/PCAs, we have identified a total of 3 factors in pre-monsoon and 4 factors in post-monsoon season, which are responsible for the whole data structure. These factors explain 77.62 and 82.39% of the total variance of the pre- and post-monsoon datasets. On the other hand, CA depicted the regions that have similar pollutants origin. The average value of synthetic pollution index of groundwater during pre-monsoon is 9.27, while during post-monsoon, it has been recorded as 8.74. On the other hand, the average values of water quality index of groundwater during pre-monsoon and post-monsoon seasons are found as 217.59 and 233.02, respectively. The study indicates that there occurs an extensive urbanization with gradual vast development of various small- and large-scale industries, which is responsible for degradation in water quality. The overall analysis reveals that the agricultural runoff, waste disposal, leaching, and irrigation with wastewater are the main causes of groundwater pollution followed by some degree of pollution from geogenic sources such as rock and soil weathering, confirmed through XRD analysis.

A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction.

BACKGROUND: Deep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level. RESULTS: We evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% 'precision' (accuracy of prediction), 97% 'recall' (sensitivity)] in predicting 'true-positive' targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low 'recall' values. Score optimizations increased the 'recall' to only 70% (corresponding 'precision': 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly 'precise' predictions. The large number of 'false negative' predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches. CONCLUSION: Although, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions.

Impact of infectious density-induced additional screening and treatment saturation on COVID-19: Modeling and cost-effective optimal control.

This study introduces a novel SI2HR model, where "I2" denotes two infectious classes representing asymptomatic and symptomatic infections, aiming to investigate and analyze the cost-effective optimal control measures for managing COVID-19. The model incorporates a novel concept of infectious density-induced additional screening (IDIAS) and accounts for treatment saturation. Furthermore, the model considers the possibility of reinfection and the loss of immunity in individuals who have previously recovered. To validate and calibrate the proposed model, real data from November-December 2022 in Hong Kong are utilized. The estimated parameters obtained from this calibration process are valuable for prediction purposes and facilitate further numerical simulations. An analysis of the model reveals that delays in screening, treatment, and quarantine contribute to an increase in the basic reproduction number R0, indicating a tendency towards endemicity. In particular, from the elasticity of R0, we deduce that normalized sensitivity indices of baseline screening rate (θ), quarantine rates (γ, αs), and treatment rate (α) are negative, which shows that delaying any of these may cause huge surge in R0, ultimately increases the disease burden. Further, by the contour plots, we note the two-parameter behavior of the infectives (both symptomatic and asymptomatic). Expanding upon the model analysis, an optimal control problem (OCP) is formulated, incorporating three control measures: precautionary interventions, boosted IDIAS, and boosted treatment. The Pontryagin's maximum principle and the forward-backward sweep method are employed to solve the OCP. The numerical simulations highlight that enhanced screening and treatment, coupled with preventive interventions, can effectively contribute to sustainable disease control. However, the cost-effectiveness analysis (CEA) conducted in this study suggests that boosting IDIAS alone is the most economically efficient and cost-effective approach compared to other strategies. The CEA results provide valuable insights into identifying specific strategies based on their cost-efficacy ranking, which can be implemented to maximize impact while minimizing costs. Overall, this research offers significant insights for policymakers and healthcare professionals, providing a framework to optimize control efforts for COVID-19 or similar epidemics in the future.