Application of a novel remote sensing ecological index (RSEI) based on geographically weighted principal component analysis for assessing the land surface ecological quality.

In evaluating the integrated remote sensing-based ecological index (RSEIPCA), principal component analysis (PCA) has been extensively utilized. However, the conventional PCA-based RSEI (RSEIPCA) cannot accurately evaluate component indicators' spatially shifting relative significance. This study presented a novel RSEI evaluation strategy based on geographically weighted principal component analysis (RSEIGWPCA) to address this deficiency. Second, compared to the classic RSEIPCA, RSEIGWPCA was tested at English Bazar and surrounding areas using two-fold validation. In this regard, the Jaccard test from a different setting and correlation analysis were utilized to examine the geographical distribution of RSEI derived by PCA and GWPCA. The validation output revealed better effectiveness of GWPCA over PCA in assessing the RSEI. The findings revealed that (i) in RSEI assessment, the spatial heterogeneity of the dataset helped to formulate individual weights by GWPCA that was not performed by PCA; and (ii) the areas having higher RSEI were primarily located around the Chatra wetland of this study area, and the areas with lower RSEI were located mainly in the industrial part. It has been concluded that RSEIGWPCA is a helpful approach in the RSEI evaluating for the regional and local scale like English bazaar city and its neighbourhood.

Structurally Dynamic Monocyte-Liposome Hybrid Vesicles as an Anticancer Drug Delivery Vehicle: A Crucial Correlation of Microscopic Elasticity and Ultrafast Dynamics.

A biomimetic cell-based carrier system based on monocyte membranes and liposomes has been designed to create a hybrid "Monocyte-LP" which inherits the surface antigens of the monocytes along with the drug encapsulation property of the liposome. Förster resonance energy transfer (FRET) and polarization gated anisotropy measurements show the stiffness of the vesicles obtained from monocyte membranes (Mons), phosphatidylcholine membranes (LP), and Monocyte-LP to follow an increasing order of Mons > Monocyte-LP > LP. The dynamics of interface bound water molecules plays a key role in the elasticity of the vesicles, which in turn imparts higher delivery efficacy to the hybrid Monocyte-LP for a model anticancer drug doxorubicin than the other two vesicles, indicating a critical balance between flexibility and rigidity for an efficient cellular uptake. The present work provides insight on the influence of elasticity of delivery vehicles for enhanced drug delivery.

Temperature-dependent dielectric relaxation measurements of (acetamide + K/Na SCN) deep eutectic solvents: Decoding the impact of cation identity via computer simulations.

The impact of successive replacement of K+ by Na+ on the megahertz-gigahertz polarization response of 0.25[fKSCN + (1 - f)NaSCN] + 0.75CH3CONH2 deep eutectic solvents (DESs) was explored via temperature-dependent (303 ≤ T/K ≤ 343) dielectric relaxation (DR) measurements and computer simulations. Both the DR measurements (0.2 ≤ ν/GHz ≤ 50) and the simulations revealed multi-Debye relaxations accompanied by a decrease in the solution static dielectric constant (ɛs) upon the replacement of K+ by Na+. Accurate measurements of the DR response of DESs below 100 MHz were limited by the well-known one-over-frequency divergence for conducting solutions. This problem was tackled in simulations by removing the zero frequency contributions arising from the ion current to the total simulated DR response. The temperature-dependent measurements revealed a much stronger viscosity decoupling of DR times for Na+-containing DES than for the corresponding K+ system. The differential scanning calorimetry measurements indicated a higher glass transition temperature for Na+-DES (∼220 K) than K+-DES (∼200 K), implying more fragility and cooperativity for the former (Na+-DES) than the latter. The computer simulations revealed a gradual decrease in the average number of H bonds (⟨nHB⟩) per acetamide molecule and increased frustrations in the average orientational order upon the replacement of K+ by Na+. Both the measured and simulated ɛs values were found to decrease linearly with ⟨nHB⟩. Decompositions of the simulated DR spectra revealed that the cation-dependent cross interaction (dipole-ion) term contributes negligibly to ɛs and appears in the terahertz regime. Finally, the simulated collective single-particle reorientational relaxations and the structural H-bond fluctuation dynamics revealed the microscopic origin of the cation identity dependence shown by the measured DR relaxation times.

LncRNA Malat1 suppresses pyroptosis and T cell-mediated killing of incipient metastatic cells.

The contribution of antitumor immunity to metastatic dormancy is poorly understood. Here we show that the long noncoding RNA Malat1 is required for tumor initiation and metastatic reactivation in mouse models of breast cancer and other tumor types. Malat1 localizes to nuclear speckles to couple transcription, splicing and mRNA maturation. In metastatic cells, Malat1 induces WNT ligands, autocrine loops to promote self-renewal and the expression of Serpin protease inhibitors. Through inhibition of caspase-1 and cathepsin G, SERPINB6B prevents gasdermin D-mediated induction of pyroptosis. In this way, SERPINB6B suppresses immunogenic cell death and confers evasion of T cell-mediated tumor lysis of incipient metastatic cells. On-target inhibition of Malat1 using therapeutic antisense nucleotides suppresses metastasis in a SERPINB6B-dependent manner. These results suggest that Malat1-induced expression of SERPINB6B can titrate pyroptosis and immune recognition at metastatic sites. Thus, Malat1 is at the nexus of tumor initiation, reactivation and immune evasion and represents a tractable and clinically relevant drug target.

Clinical effects of 2-DG drug restraining SARS-CoV-2 infection: A fractional order optimal control study.

Fractional calculus is very convenient tool in modeling of an emergent infectious disease system comprising previous disease states, memory of disease patterns, profile of genetic variation etc. Significant complex behaviors of a disease system could be calibrated in a proficient manner through fractional order derivatives making the disease system more realistic than integer order model. In this study, a fractional order differential equation model is developed in micro level to gain perceptions regarding the effects of host immunological memory in dynamics of SARS-CoV-2 infection. Additionally, the possible optimal control of the infection with the help of an antiviral drug, viz. 2-DG, has been exemplified here. The fractional order optimal control would enable to employ the proper administration of the drug minimizing its systematic cost which will assist the health policy makers in generating better therapeutic measures against SARS-CoV-2 infection. Numerical simulations have advantages to visualize the dynamical effects of the immunological memory and optimal control inputs in the epidemic system.

How do the contaminated environment influence the transmission dynamics of COVID-19 pandemic?

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus that first appeared in Wuhan city and then globally. The COVID-19 pandemic exudes public health and socio-economic burden globally. Mathematical modeling plays a significant role to comprehend the transmission dynamics and controlling factors of rapid spread of the disease. Researchers focus on the human-to-human transmission of the virus but the SARS-CoV-2 virus also contaminates the environment. In this study we proposed a nonlinear mathematical model for the COVID-19 pandemic to analyze the transmission dynamics of the disease in India. We have also incorporated the environment contamination by the infected individuals as the population density is very high in India. The model is fitted and parameterized using daily new infection data from India. Analytical study of the proposed COVID-19 model, including feasibility of critical points and their stability reveals that the infection-free steady state is stable if the basic reproduction number is less than unity otherwise the system shows significant outbreak. Numerical illustrations demonstrates that if the rate of environment contamination increased then the number of infected persons also increased. But if the environment is disinfected by sanitization then the number of infected persons cannot drastically increase.

SIK2 inhibition enhances PARP inhibitor activity synergistically in ovarian and triple-negative breast cancers.

Poly(ADP-ribose) polymerase inhibitors (PARP inhibitors) have had an increasing role in the treatment of ovarian and breast cancers. PARP inhibitors are selectively active in cells with homologous recombination DNA repair deficiency caused by mutations in BRCA1/2 and other DNA repair pathway genes. Cancers with homologous recombination DNA repair proficiency respond poorly to PARP inhibitors. Cancers that initially respond to PARP inhibitors eventually develop drug resistance. We have identified salt-inducible kinase 2 (SIK2) inhibitors, ARN3236 and ARN3261, which decreased DNA double-strand break (DSB) repair functions and produced synthetic lethality with multiple PARP inhibitors in both homologous recombination DNA repair deficiency and proficiency cancer cells. SIK2 is required for centrosome splitting and PI3K activation and regulates cancer cell proliferation, metastasis, and sensitivity to chemotherapy. Here, we showed that SIK2 inhibitors sensitized ovarian and triple-negative breast cancer (TNBC) cells and xenografts to PARP inhibitors. SIK2 inhibitors decreased PARP enzyme activity and phosphorylation of class-IIa histone deacetylases (HDAC4/5/7). Furthermore, SIK2 inhibitors abolished class-IIa HDAC4/5/7-associated transcriptional activity of myocyte enhancer factor-2D (MEF2D), decreasing MEF2D binding to regulatory regions with high chromatin accessibility in FANCD2, EXO1, and XRCC4 genes, resulting in repression of their functions in the DNA DSB repair pathway. The combination of PARP inhibitors and SIK2 inhibitors provides a therapeutic strategy to enhance PARP inhibitor sensitivity for ovarian cancer and TNBC.

Mathematical modeling and optimal intervention strategies of the COVID-19 outbreak.

34,354,966 active cases and 460,787 deaths because of COVID-19 pandemic were recorded on November 06, 2021, in India. To end this ongoing global COVID-19 pandemic, there is an urgent need to implement multiple population-wide policies like social distancing, testing more people and contact tracing. To predict the course of the pandemic and come up with a strategy to control it effectively, a compartmental model has been established. The following six stages of infection are taken into consideration: susceptible (S), asymptomatic infected (A), clinically ill or symptomatic infected (I), quarantine (Q), isolation (J) and recovered (R), collectively termed as SAIQJR. The qualitative behavior of the model and the stability of biologically realistic equilibrium points are investigated in terms of the basic reproduction number. We performed sensitivity analysis with respect to the basic reproduction number and obtained that the disease transmission rate has an impact in mitigating the spread of diseases. Moreover, considering the non-pharmaceutical and pharmaceutical intervention strategies as control functions, an optimal control problem is implemented to mitigate the disease fatality. To reduce the infected individuals and to minimize the cost of the controls, an objective functional has been constructed and solved with the aid of Pontryagin's maximum principle. The implementation of optimal control strategy at the start of a pandemic tends to decrease the intensity of epidemic peaks, spreading the maximal impact of an epidemic over an extended time period. Extensive numerical simulations show that the implementation of intervention strategy has an impact in controlling the transmission dynamics of COVID-19 epidemic. Further, our numerical solutions exhibit that the combination of three controls are more influential when compared with the combination of two controls as well as single control. Therefore, the implementation of all the three control strategies may help to mitigate novel coronavirus disease transmission at this present epidemic scenario. Supplementary Information: The online version supplementary material available at 10.1007/s11071-022-07235-7.

Effect of an antiviral drug control and its variable order fractional network in host COVID-19 kinetics.

In December 2019, a novel coronavirus disease (COVID-19) appeared in Wuhan, China. After that, it spread rapidly all over the world. Novel coronavirus belongs to the family of Coronaviridae and this new strain is called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epithelial cells of our throat and lungs are the main target area of the SARS-CoV-2 virus which leads to COVID-19 disease. In this article, we propose a mathematical model for examining the effects of antiviral treatment over viral mutation to control disease transmission. We have considered here three populations namely uninfected epithelial cells, infected epithelial cells, and SARS-CoV-2 virus. To explore the model in light of the optimal control-theoretic strategy, we use Pontryagin's maximum principle. We also illustrate the existence of the optimal control and the effectiveness of the optimal control is studied here. Cost-effectiveness and efficiency analysis confirms that time-dependent antiviral controlled drug therapy can reduce the viral load and infection process at a low cost. Numerical simulations have been done to illustrate our analytical findings. In addition, a new variable-order fractional model is proposed to investigate the effect of antiviral treatment over viral mutation to control disease transmission. Considering the superiority of fractional order calculus in the modeling of systems and processes, the proposed variable-order fractional model can provide more accurate insight for the modeling of the disease. Then through the genetic algorithm, optimal treatment is presented, and its numerical simulations are illustrated.

Correction to: Effect of an antiviral drug control and its variable order fractional network in host COVID-19 kinetics.

[This corrects the article DOI: 10.1140/epjs/s11734-022-00454-4.].

Dynamical demeanour of SARS-CoV-2 virus undergoing immune response mechanism in COVID-19 pandemic.

COVID-19 is caused by the increase of SARS-CoV-2 viral load in the respiratory system. Epithelial cells in the human lower respiratory tract are the major target area of the SARS-CoV-2 viruses. To fight against the SARS-CoV-2 viral infection, innate and thereafter adaptive immune responses be activated which are stimulated by the infected epithelial cells. Strong immune response against the COVID-19 infection can lead to longer recovery time and less severe secondary complications. We proposed a target cell-limited mathematical model by considering a saturation term for SARS-CoV-2-infected epithelial cells loss reliant on infected cells level. The analytical findings reveal the conditions for which the system undergoes transcritical bifurcation and alternation of stability for the system around the steady states happens. Due to some external factors, while the viral reproduction rate exceeds its certain critical value, backward bifurcation and reinfection may take place and to inhibit these complicated epidemic states, host immune response, or immunopathology would play the essential role. Numerical simulation has been performed in support of the analytical findings.

Intercellular nanotubes mediate mitochondrial trafficking between cancer and immune cells.

Cancer progresses by evading the immune system. Elucidating diverse immune evasion strategies is a critical step in the search for next-generation immunotherapies for cancer. Here we report that cancer cells can hijack the mitochondria from immune cells via physical nanotubes. Mitochondria are essential for metabolism and activation of immune cells. By using field-emission scanning electron microscopy, fluorophore-tagged mitochondrial transfer tracing and metabolic quantification, we demonstrate that the nanotube-mediated transfer of mitochondria from immune cells to cancer cells metabolically empowers the cancer cells and depletes the immune cells. Inhibiting the nanotube assembly machinery significantly reduced mitochondrial transfer and prevented the depletion of immune cells. Combining a farnesyltransferase and geranylgeranyltransferase 1 inhibitor, namely, L-778123, which partially inhibited nanotube formation and mitochondrial transfer, with a programmed cell death protein 1 immune checkpoint inhibitor improved the antitumour outcomes in an aggressive immunocompetent breast cancer model. Nanotube-mediated mitochondrial hijacking can emerge as a novel target for developing next-generation immunotherapy agents for cancer.

Room-Temperature One-Step Synthesis of Silver/Reduced Graphene Oxide Nanocomposites as an Excellent Microwave Absorber.

The present study is focused on room-temperature synthesis carried out by reduction of an aqueous silver nitrate (AgNO3) and AgNO3/graphene oxide (GO) dispersion using a low-cost commercial Fehling B solution in one step to form silver quantum dots (Ag QDs) and their Ag/reduced graphene oxide (Ag/RGO) nanocomposites and their characterization. The crystallinity, surface chemistry, structural, and morphological studies indicated the formation of crystalline small-sized quasispherical-functionalized Ag particles distributed uniformly on the surface of RGO. The conductivity measurements further showed an improvement in the conductivity of Ag/RGO nanocomposites as compared to neat Ag QDs. Our findings showed that Ag/RGO nanocomposites prepared by using 0.055 wt % of GO exhibited a total enhanced electromagnetic interference (EMI)-shielding efficiency (SET) of ∼39.2-42.3 dB (2-8 GHz) with a maximum value of ∼43.8 dB at 7. 5 GHz due to conduction loss, an interconnected conducting network, and a synergistic effect, and it followed an absorption mechanism. Furthermore, this superior absorption-dominated shielding conferred reflection loss (RL) in the range of -79 to -82.5 dB with a RL minima of -88 dB at 7.5 GHz, considering an effective absorption bandwidth of ∼6 GHz with 99.9% absorptivity. It is anticipated that Ag/RGO nanocomposites prepared in one step at room temperature could find potential EMI-shielding applications.

Nanotherapeutic approaches to overcome distinct drug resistance barriers in models of breast cancer.

Targeted delivery of drugs to tumor cells, which circumvent resistance mechanisms and induce cell killing, is a lingering challenge that requires innovative solutions. Here, we provide two bioengineered strategies in which nanotechnology is blended with cancer medicine to preferentially target distinct mechanisms of drug resistance. In the first 'case study', we demonstrate the use of lipid-drug conjugates that target molecular signaling pathways, which result from taxane-induced drug tolerance via cell surface lipid raft accumulations. Through a small molecule drug screen, we identify a kinase inhibitor that optimally destroys drug tolerant cancer cells and conjugate it to a rationally-chosen lipid scaffold, which enhances anticancer efficacy in vitro and in vivo. In the second 'case study', we address resistance mechanisms that can occur through exocytosis of nanomedicines. Using adenocarcinoma HeLa and MCF-7 cells, we describe the use of gold nanorod and nanoporous vehicles integrated with an optical antenna for on-demand, photoactivation at ~650 nm enabling release of payloads into cells including cytotoxic anthracyclines. Together, these provide two approaches, which exploit engineering strategies capable of circumventing distinct resistance barriers and induce killing by multimodal, including nanophotonic mechanisms.

SARS-CoV-2 infection with lytic and non-lytic immune responses: A fractional order optimal control theoretical study.

In this research article, we establish a fractional-order mathematical model to explore the infections of the coronavirus disease (COVID-19) caused by the novel SARS-CoV-2 virus. We introduce a set of fractional differential equations taking uninfected epithelial cells, infected epithelial cells, SARS-CoV-2 virus, and CTL response cell accounting for the lytic and non-lytic effects of immune responses. We also include the effect of a commonly used antiviral drug in COVID-19 treatment in an optimal control-theoretic approach. The stability of the equilibria of the fractional ordered system using qualitative theory. Numerical simulations are presented using an iterative scheme in Matlab in support of the analytical results.

Mathematical modeling of the COVID-19 pandemic with intervention strategies.

Mathematical modeling plays an important role to better understand the disease dynamics and designing strategies to manage quickly spreading infectious diseases in lack of an effective vaccine or specific antivirals. During this period, forecasting is of utmost priority for health care planning and to combat COVID-19 pandemic. In this study, we proposed and extended classical SEIR compartment model refined by contact tracing and hospitalization strategies to explain the COVID-19 outbreak. We calibrated our model with daily COVID-19 data for the five provinces of India namely, Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal and the overall India. To identify the most effective parameters we conduct a sensitivity analysis by using the partial rank correlation coefficients techniques. The value of those sensitive parameters were estimated from the observed data by least square method. We performed sensitivity analysis for R 0 to investigate the relative importance of the system parameters. Also, we computed the sensitivity indices for R 0 to determine the robustness of the model predictions to parameter values. Our study demonstrates that a critically important strategy can be achieved by reducing the disease transmission coefficient ß s and clinical outbreak rate q a to control the COVID-19 outbreaks. Performed short-term predictions for the daily and cumulative confirmed cases of COVID-19 outbreak for all the five provinces of India and the overall India exhibited the steady exponential growth of some states and other states showing decays of daily new cases. Long-term predictions for the Republic of India reveals that the COVID-19 cases will exhibit oscillatory dynamics. Our research thus leaves the option open that COVID-19 might become a seasonal disease. Our model simulation demonstrates that the COVID-19 cases across India at the end of September 2020 obey a power law.

Comparison of [11C]UCB-J and [18F]FDG PET in Alzheimer's disease: A tracer kinetic modeling study.

[11C]UCB-J PET for synaptic vesicle glycoprotein 2 A (SV2A) has been proposed as a suitable marker for synaptic density in Alzheimer's disease (AD). We compared [11C]UCB-J binding for synaptic density and [18F]FDG uptake for metabolism (correlated with neuronal activity) in 14 AD and 11 cognitively normal (CN) participants. We assessed both absolute and relative outcome measures in brain regions of interest, i.e., K1 or R1 for [11C]UCB-J perfusion, VT (volume of distribution) or DVR to cerebellum for [11C]UCB-J binding to SV2A; and Ki or KiR to cerebellum for [18F]FDG metabolism. [11C]UCB-J binding and [18F]FDG metabolism showed a similar magnitude of reduction in the medial temporal lobe of AD -compared to CN participants. However, the magnitude of reduction of [11C]UCB-J binding in neocortical regions was less than that observed with [18F]FDG metabolism. Inter-tracer correlations were also higher in the medial temporal regions between synaptic density and metabolism, with lower correlations in neocortical regions. [11C]UCB-J perfusion showed a similar pattern to [18F]FDG metabolism, with high inter-tracer regional correlations. In summary, we conducted the first in vivo PET imaging of synaptic density and metabolism in the same AD participants and reported a concordant reduction in medial temporal regions but a discordant reduction in neocortical regions.

Nanoengineered Disruption of Heat Shock Protein 90 Targets Drug-Induced Resistance and Relieves Natural Killer Cell Suppression in Breast Cancer.

Drug-induced resistance, or tolerance, is an emerging yet poorly understood failure of anticancer therapy. The interplay between drug-tolerant cancer cells and innate immunity within the tumor, the consequence on tumor growth, and therapeutic strategies to address these challenges remain undescribed. Here, we elucidate the role of taxane-induced resistance on natural killer (NK) cell tumor immunity in triple-negative breast cancer (TNBC) and the design of spatiotemporally controlled nanomedicines, which boost therapeutic efficacy and invigorate "disabled" NK cells. Drug tolerance limited NK cell immune surveillance via drug-induced depletion of the NK-activating ligand receptor axis, NK group 2 member D, and MHC class I polypeptide-related sequence A, B. Systems biology supported by empirical evidence revealed the heat shock protein 90 (Hsp90) simultaneously controls immune surveillance and persistence of drug-treated tumor cells. On the basis of this evidence, we engineered a "chimeric" nanotherapeutic tool comprising taxanes and a cholesterol-tethered Hsp90 inhibitor, radicicol, which targets the tumor, reduces tolerance, and optimally reprimes NK cells via prolonged induction of NK-activating ligand receptors via temporal control of drug release in vitro and in vivo. A human ex vivo TNBC model confirmed the importance of NK cells in drug-induced death under pressure of clinically approved agents. These findings highlight a convergence between drug-induced resistance, the tumor immune contexture, and engineered approaches that consider the tumor and microenvironment to improve the success of combinatorial therapy. SIGNIFICANCE: This study uncovers a molecular mechanism linking drug-induced resistance and tumor immunity and provides novel engineered solutions that target these mechanisms in the tumor and improve immunity, thus mitigating off-target effects.

A mathematical model for COVID-19 transmission dynamics with a case study of India.

The ongoing COVID-19 has precipitated a major global crisis, with 968,117 total confirmed cases, 612,782 total recovered cases and 24,915 deaths in India as of July 15, 2020. In absence of any effective therapeutics or drugs and with an unknown epidemiological life cycle, predictive mathematical models can aid in understanding of both coronavirus disease control and management. In this study, we propose a compartmental mathematical model to predict and control the transmission dynamics of COVID-19 pandemic in India with epidemic data up to April 30, 2020. We compute the basic reproduction number R 0, which will be used further to study the model simulations and predictions. We perform local and global stability analysis for the infection free equilibrium point E 0 as well as an endemic equilibrium point E* with respect to the basic reproduction number R 0. Moreover, we showed the criteria of disease persistence for R 0 > 1. We conduct a sensitivity analysis in our coronavirus model to determine the relative importance of model parameters to disease transmission. We compute the sensitivity indices of the reproduction number R 0 (which quantifies initial disease transmission) to the estimated parameter values. For the estimated model parameters, we obtained R 0 = 1.6632 , which shows the substantial outbreak of COVID-19 in India. Our model simulation demonstrates that the disease transmission rate ßs is more effective to mitigate the basic reproduction number R 0. Based on estimated data, our model predict that about 60 days the peak will be higher for COVID-19 in India and after that the curve will plateau but the coronavirus diseases will persist for a long time.

Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches.

This study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen's Innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and pre-monsoon seasons. Furthermore, the significant negative trend (-8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901-1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions' also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand.