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Protosticta Selys, 1885 is a speciose genus of damselflies distributed in the tropical and subtropical forests of Asia. During an ongoing study to document the odonate diversity of the Western Ghats, we came across a colony of Protosticta species in Wayanad, Kerala, that appeared different from all other species hitherto described. We describe this population as a new species after detailed morphological comparison with closely similar species occurring in the region.

To compare the whole genomic microRNA (miRNA) between the selective fetal growth restriction (sFGR) twin and the normally growing (control) co-twin in monochorionic (MC) twin pregnancies. MC twin pregnancies with or without sFGR were recruited, and their placental miRNAs were profiled by microarray. The ratio of the placental miRNA of the sFGR twin to that of the normally larger co-twin were calculated and compared to that of the control twin pairs. The miRNA microarray intensity amongst normal and sFGR large and small twins are shown. The expression data presented here will facilitate other researchers who are working on placental regulation and mechanism in pregnancy complicated by fetal growth restriction.The dataset supports the research article entitle “Whole genome miRNA profiling revealed miR-199a as potential placental pathogenesis of selective fetal growth restriction in monochorionic twin pregnancies” [1].

Traumatic brain injuries (TBI) present a major public health challenge, demanding an in-depth understanding of age-specific symptoms and risk factors. Aging not only significantly influences brain function and plasticity but also elevates the risk of hospitalizations and death following TBIs. Repetitive mild TBIs (rmTBI) compound these issues, resulting in cumulative and long-term brain damage in the brain. In this study, we investigate the impact of age on brain network changes and white matter properties following rmTBI by employing a multi-modal approach that integrates resting-state functional magnetic resonance imaging (rsfMRI), graph theory analysis, diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI). Our hypothesis is that the effects of rmTBI are worsened in aged animals, with this group showing more pronounced alterations in brain connectivity and white matter structure. Utilizing the closed-head impact model of engineered rotational acceleration (CHIMERA) model, we conducted rmTBIs or sham (control) procedures on young (2.5–3-months-old) and aged (22-months-old) male and female mice to model high-risk groups. Functional and structural imaging unveiled age-related reductions in communication efficiency between brain regions, while injuries induced opposhigh-risking effects on the small-world index across age groups, influencing network segregation. Functional connectivity analysis also identified alterations in 79 out of 148 brain regions by age, treatment (sham vs. rmTBI), or their interaction. Injuries exerted pronounced effects on sensory integration areas, including insular and motor cortices. Age-related disruptions in white matter integrity were observed, indicating alterations in various diffusion directions (mean diffusivity, radial diffusivity, axial diffusivity, and fractional anisotropy) and density neurite properties (dispersion index, intracellular and isotropic volume fraction). Neuroinflammation, assessed through Iba-1 and GFAP markers, correlated with higher dispersion in the optic tract, suggesting a neuroinflammatory response in injured aged animals compared to sham aged. These findings offer insight into the interplay between age, injuries, and brain connectivity, shedding light on the long-term consequences of rmTBI.

The ovarian tumor microenvironment plays a critical yet is poorly understood role in the regulation of cancer cell behaviors including proliferation, migration, and response to chemotherapy treatments. Ovarian cancer is the deadliest gynecological cancer, due to diagnosis at late stages of the disease and increased resistance to chemotherapies for recurrent disease. Understanding how the tumor microenvironment (TME) interacts with biomechanical forces to drive changes to ovarian cancer cell behaviors could elucidate novel treatment strategies for this patient population. Additionally, limitations in current preclinical models of the ovarian TME do not permit investigation of crosstalk between signaling pathways and mechanical forces. Our study focused on uncovering how strains and hyaluronic acid (HA) interact to signal through the CD44 receptor to alter ovarian cancer cell growth, migration, and response to a commonly used chemotherapy, paclitaxel. Using an advanced 3D in vitro model, we were able to identify how interactions of strain and HA as in the TME synergistically drive enhanced proliferation and migration in an ovarian tumor model line, while decreasing response to paclitaxel treatment. This study demonstrates the importance of elucidating how the mechanical forces present in the ovarian TME drive disease progression and response to treatment.

Finasteride, a steroid 5-alpha reductase inhibitor, is commonly used for the treatment of benign prostatic hyperplasia and hair loss. However, despite continued use, its environmental implications have not been thoroughly investigated. Thus, we investigated the acute and chronic adverse impacts of finasteride on Daphnia magna, a crucial planktonic crustacean in freshwater ecosystems selected as bioindicator organism for understanding the ecotoxicological effects. Chronic exposure (for 23 days) to finasteride negatively affected development and reproduction, leading to reduced fecundity, delayed first brood, reduced growth, and reduced neonate size. Additionally, acute exposure (< 24 h) caused decreased expression levels of genes crucial for reproduction and development, especially EcR-A/B (ecdysone receptors), Jhe (juvenile hormone esterase), and Vtg2 (vitellogenin), with oxidative stress-related genes. Untargeted lipidomics/metabolomic analyses revealed lipidomic alteration, including 19 upregulated and 4 downregulated enriched lipid ontology categories, and confirmed downregulation of metabolites. Pathway analysis implicated significant effects on metabolic pathways, including the pentose phosphate pathway, histidine metabolism, beta-alanine metabolism, as well as alanine, aspartate, and glutamate metabolism. This comprehensive study unravels the intricate molecular and metabolic responses of D. magna to finasteride exposure, underscoring the multifaceted impacts of this anti-androgenic compound on a keystone species of freshwater ecosystems. The findings emphasize the importance of understanding the environmental repercussions of widely used pharmaceuticals to protect biodiversity in aquatic ecosystems.

This study presents a mathematical model to investigate the patterns of transmission in lymphatic filariasis. The model considers chronic, acute, and asymptomatic individuals and integrates key control strategies. Random synthetic data is generated robustly through numerical solutions to closely replicate real-world scenarios and encompass uncertainties. The synthetic data adheres to a Gaussian distribution to ensure validity and reliability. Following the derivation of the basic and effective reproduction number using the next generation matrix approach, Latin Hypercube Sampling (LHS) and the Partial Rank Correlation Coefficient (PRCC) algorithm is utilized to assess the parameters that significantly influence the model outputs. The study examine the trajectories of different population compartments through numerical simulations over time, with particular emphasis on the role played by asymptomatic individuals in the transmission of the disease. To assess the potential for disease elimination, the study introduces a range of strategies involving protective measures, treatment interventions, and mosquito control. These strategies are determined through sensitivity analysis. The findings demonstrate that the simultaneous implementation of all control measures has a noteworthy effect in managing lymphatic filariasis. In conclusion, the proposed model enhances understanding of lymphatic filariasis dynamics and informs effective control strategies.

Plutella xylostella, commonly called Diamondback moth (DBM), a highly destructive and rapidly spreading agricultural pest originally from Europe. This pest poses a significant threat to global food security, with estimates suggesting that periodic outbreaks of Diamondback moth lead to annual crop losses of up to $US 4−5 billion worldwide. Given the potential for such substantial losses, it is crucial to employ various methods and techniques to understand the factors affecting the interaction between Diamondback moths and cabbage plants, which, in turn, impact cabbage biomass. In this paper, we propose a deterministic ecological model to capture the dynamics of Plutella xylostella infestations in cabbage biomass. The model is designed based on the life cycle stages of the pest, aiming at targeting the specific stage effectively. The synthetic data is generated using Least Square Algorithm through addition of Gaussian noise into numerically obtained values from existing literature to simulate real-world data. Global sensitivity analysis was done through Latin Hypercube sampling, highlights the significance of parameters such as ψ,αE and δ positively influence the growth of the diamondback moth in a cabbage biomass. In light of these findings, the study proposes that control strategies should be specifically directed towards these sensitive parameters. By doing so, we mitigate the pest population and enhance cabbage production.

Microbiologically induced corrosion (MIC) is a prominent issue in high-producer main oil lines (MOL) despite the stringent mechanical and chemical treatment due to Sulfate-reducing bacteria (SRB). Monthly corrosion monitoring shows the number of bacteria on the pig body and in seawater remains significant. The investigation corresponds with the dormancy and duplication of SRB retains in siloes due to uncontrolled parameters such as injection schedule and the ideal biocide dosage. While investigating and predicting the resistant bacteria that may threaten the pipeline is an attractive discussion, modelling the corrosion mitigation using a corrosion test is equally essential to predict the effectiveness of biocides. Biocide performance was evaluated in a few test solutions from two locations in one of the well-established oil company producers in northwest Java. The corresponding time-kill test using Tetrakis (hydroxymethyl) Phosphonium Sulfate (THPS) adheres to NACE TM- 0194 (2014) standard to obtain the optimum biocide dosing of the bacteria solution. Biofilm and micro-pitting were identified by evaluating the blank and 1000 μg/L 60% THPS solutions using a Scanning Electronic Microscope (SEM). Based on the time kill test, the inactive bacteria in dormancy after injection 500 μg/L and 1000 μg/L THPS 60%. The wheel corrosion test shows ineffective injection increases chemical acidity and corrosion rate. The SEM result confirms the time-kill test and indicates the possible sessile bacteria and biotic pitting. This work exemplifies the comprehensive engagement between the field operation and experimental work to quicken the MIC analysis and recommend a viable strategy for the parameter field variation.

This paper presents a numerical method for estimating four physical parameters of a single-diode circuit model based on manufacturer’s datasheet. A system of four non-linear equations are formed based on three key points of PV characteristics. The photocurrent, saturation current, ideality factor and the series resistance are solved iteratively using the proposed method. The suggested method is validated using RTC France solar cell, Chloride CHL285P and Photowatt PWP210 modules and the results are verified with respect to the in-field outdoor measurements. The proposed method shows a good agreement with the experimental data. Lastly, The model chosen is simulated under MATLB environment to assess the effects of external physical weather conditions, that is, temperature and solar irradiance. The advantage of the proposed method with respect of existing numerical techniques is that it converged faster than the widely used Newton method. Modeling of PV cell/module is essential in predicting performance of photovoltaic generators at any operating condition.

The demand for transportation, driven by an increasing global population, is continuously rising. This has led to a higher number of vehicles on the road and an increased reliance on fossil fuels. Consequently, the rise in atmospheric carbon dioxide (CO2) levels has contributed to global warming. Therefore, it is important to consider sustainable transportation practices to meet climate change mitigation targets. In this research paper, a non-linear mathematical model is developed to study the dynamics of atmospheric CO2 concentration in relation to human population, economic activities, forest biomass, and vehicle population. The developed model is analyzed qualitatively to understand the long-term behavior of the system's dynamics. Model parameters are fitted to actual data of world population, human economic activities, atmospheric CO2, forest biomass, and vehicle population. It is shown that increased vehicular CO2 emissions have a potential contribution to the increase in atmospheric CO2 and the decline of human population. Numerical simulations are carried out to verify the analytical findings and we performed global sensitivity analysis to explore the impacts of different sensitive parameters on the CO2 dynamics.

Lymphatic filariasis is a neglected tropical disease which poses public health concern and socio-economic challenges in developing and low-income countries. In this paper, we formulate a deterministic mathematical model for transmission dynamics of lymphatic filariasis to generate data by white noise and use least square method to estimate parameter values. The validity of estimated parameter values is tested by Gaussian distribution method. The residuals of model outputs are normally distributed and hence can be used to study the dynamics of Lymphatic filariasis. After deriving the basic reproduction number, R0 by the next generation matrix approach, the Partial Rank Correlation Coefficient is employed to explore which parameters significantly affect and most influential to the model outputs. The analysis for equilibrium states shows that the Lymphatic free equilibrium is globally asymptotically stable when the basic reproduction number is less a unity and endemic equilibrium is globally asymptotically stable when R0≥1. The findings reveal that rate of human infection, recruitment rate of mosquitoes increase the average new infections for Lymphatic filariasis. Moreover, asymptomatic individuals contribute significantly in the transmission of Lymphatic filariasis.

This paper presents a system of non-linear differential equations describing the depletion of forest biomass and forest-dependent wildlife population caused by human population and its associated activities. The model incorporates the imprecise nature of the parameters, which are treated as triangular fuzzy numbers to reflect the inherent uncertainty. We utilised α cut to transform these imprecise parameters into intervals. Subsequently, employing the principles of interval mathematics, we effectively converted the related differential equation into a pair of distinct differential equations. By leveraging the signed distance of the fuzzy numbers, we further simplified the equations, resulting in a single differential equation, which led to the formulation of a defuzzified model. The existence of equilibrium points with their stability behaviour is presented. Furthermore, the existence of trans-critical bifurcation is analysed. Through numerical simulations, we observe significant differences between the solutions of system in crisp and fuzzy environments. These findings highlight the importance of using fuzzy models to accurately represent the dynamics of complex natural systems. Consequently, we conclude that fuzzy models provide a trustworthy representation of the dynamics of complex natural systems.