Interconnection between density-regulation and stability in competitive ecological network.

In natural ecosystems, species can be characterized by the nonlinear density-dependent self-regulation of their growth profile. Species of many taxa show a substantial density-dependent reduction for low population size. Nevertheless, many show the opposite trend; density regulation is minimal for small populations and increases significantly when the population size is near the carrying capacity. The theta-logistic growth equation can portray the intraspecific density regulation in the growth profile, theta being the density regulation parameter. In this study, we examine the role of these different growth profiles on the stability of a competitive ecological community with the help of a mathematical model of competitive species interactions. This manuscript deals with the random matrix theory to understand the stability of the classical theta-logistic models of competitive interactions. Our results suggest that having more species with strong density dependence, which self-regulate at low densities, leads to more stable communities. With this, stability also depends on the complexity of the ecological network. Species network connectance (link density) shows a consistent trend of increasing stability, whereas community size (species richness) shows a context-dependent effect. We also interpret our results from the aspect of two different life history strategies: r and K-selection. Our results show that the stability of a competitive network increases with the fraction of r-selected species in the community. Our result is robust, irrespective of different network architectures.

Extremely oligotrophic and complex-carbon-degrading microaerobic bacteria from Arabian Sea oxygen minimum zone sediments.

Sediments underlying marine hypoxic zones are huge sinks of unreacted complex organic matter, where despite acute O2 limitation, obligately aerobic bacteria thrive, and steady depletion of organic carbon takes place within a few meters below the seafloor. However, little knowledge exists about the sustenance and complex carbon degradation potentials of aerobic chemoorganotrophs in these sulfidic ecosystems. We isolated and characterized a number of aerobic bacterial chemoorganoheterotrophs from across a ~ 3 m sediment horizon underlying the perennial hypoxic zone of the eastern Arabian Sea. High levels of sequence correspondence between the isolates' genomes and the habitat's metagenomes and metatranscriptomes illustrated that the strains were widespread and active across the sediment cores explored. The isolates catabolized several complex organic compounds of marine and terrestrial origins in the presence of high or low, but not zero, O2. Some of them could also grow anaerobically on yeast extract or acetate by reducing nitrate and/or nitrite. Fermentation did not support growth, but enabled all the strains to maintain a fraction of their cell populations over prolonged anoxia. Under extreme oligotrophy, limited growth followed by protracted stationary phase was observed for all the isolates at low cell density, amid high or low, but not zero, O2 concentration. While population control and maintenance could be particularly useful for the strains' survival in the critically carbon-depleted layers below the explored sediment depths (core-bottom organic carbon: 0.5-1.0% w/w), metagenomic data suggested that in situ anoxia could be surmounted via potential supplies of cryptic O2 from previously reported sources such as Nitrosopumilus species.

Spatial characterization and quantification of CD40 expression across cancer types.

BACKGROUND: CD40, a TNF receptor family member, is expressed by a variety of immune cells and is involved in the activation of both adaptive and innate immune responses. Here, we used quantitative immunofluorescence (QIF) to evaluate CD40 expression on the tumor epithelium of solid tumors in large patient cohorts of lung, ovarian, and pancreatic cancers. METHODS: Tissue samples from nine different solid tumors (bladder, breast, colon, gastric, head and neck, non-small cell lung cancer (NSCLC), ovarian, pancreatic and renal cell carcinoma), constructed in tissue microarray format, were initially assessed for CD40 expression by QIF. CD40 expression was then evaluated on the large available patient cohorts for three of the tumor types demonstrating high CD40 positivity rate; NSCLC, ovarian and pancreatic cancer. The prognostic impact of CD40 expression on tumor cells was also investigated. RESULTS: CD40 expression on tumor cells was found to be common, with 80% of the NSCLC population, 40% of the ovarian cancer population, and 68% of the pancreatic adenocarcinoma population displaying some degree of CD40 expression on cancer cells. All of three of these cancer types displayed considerable intra-tumoral heterogeneity of CD40 expression, as well as partial correlation between expression of CD40 on tumor cells and on surrounding stromal cells. CD40 was not found to be prognostic for overall survival in NSCLC, ovarian cancer, or pancreatic adenocarcinoma. CONCLUSIONS: The high percentage of tumor cells expressing CD40 in each of these solid tumors should be considered in the development of therapeutic agents designed to target CD40.

Revisiting and redefining return rate for determination of the precise growth status of a species.

Growth curve models play an instrumental role in quantifying the growth of biological processes and have immense practical applications across all disciplines. The most popular growth metric to capture the species fitness is the "Relative Growth Rate" in this domain. The different growth laws, such as exponential, logistic, Gompertz, power, and generalized Gompertz or generalized logistic, can be characterized based on the monotonic behavior of the relative growth rate (RGR) to size or time. Thus, in this case, species fitness can be determined truly through RGR. However, in nature, RGR is often non-monotonic and specifically bell-shaped, especially in the situation when a species is adapting to a new environment [1]. In this case, species may experience with the same fitness (RGR) for two different time points. The species precise growth and maturity status cannot be determined from this RGR function. The instantaneous maturity rate (IMR), as proposed by [2], helps to determine the correct maturity status of the species. Nevertheless, the metric IMR suffers from severe drawbacks; (i) IMR is intractable for all non-integer values of a specific parameter. (ii) The measure depends on a model parameter. The mathematical expression of IMR possesses the term "carrying capacity" which is unknown to the experimenter. (iii) Note that for identifying the precise growth status of a species, it is also necessary to understand its response when the populations are deflected from their equilibrium position at carrying capacity. This is an established concept in population biology, popularly known as the return rate. However, IMR does not provide information on the species deflection rate at the steady state. Hence, we propose a new growth measure connected with the species return rate, termed the "reverse of relative of relative growth rate" (henceforth, RRRGR), which is treated as a proxy for the IMR, having similar mathematical properties. Finally, we introduce a stochastic RRRGR model for specifying precise species growth and status of maturity. We illustrate the model through numerical simulations and real fish data. We believe that this study would be helpful for fishery biologists in regulating the favorable conditions of growth so that the species can reach a steady state with optimum effort.

Instantaneous maturity rate: a novel and compact characterization of biological growth curve models.

Modeling and analysis of biological growth curves are an age-old study area in which much effort has been dedicated to developing new growth equations. Recent efforts focus on identifying the correct model from a large number of equations. The relative growth rate (RGR), developed by Fisher (1921), has largely been used in the statistical inference of biological growth curve models. It is convenient to express growth equations using RGR, where RGR can be expressed as functions of size or time. Even though RGR is model invariant, it has limitations when it comes to identifying actual growth patterns. By proposing interval-specific rate parameters (ISRPs), Pal et al. (2018) appeared to solve this problem. The ISRP is based on the mathematical structure of the growth equations. Therefore, it is not model invariant. The current effort is to develop a measure of growth that is model invariant like RGR and shares the advantages of ISRP. We propose a new measure of growth, which we call instantaneous maturity rate (IMR). IMR is model invariant, which allows it to distinguish growth patterns more clearly than RGR. IMR is also scale-invariant and can take several forms including increasing, decreasing, constant, sigmoidal, bell-shaped, and bathtub. A wide range of possible IMR shapes makes it possible to identify different growth curves. The estimation procedure of IMR under a stochastic setup has been developed. Statistical properties of empirical IMR estimators have also been investigated in detail. In addition to extensive simulation studies, real data sets have been analyzed to prove the utility of IMR.

COVID-19 pandemic models revisited with a new proposal: Plenty of epidemiological models outcast the simple population dynamics solution.

We have put an effort to estimate the number of publications related to the modelling aspect of the corona pandemic through the web search with the corona associated keywords. The survey reveals that plenty of epidemiological models outcast the simple population dynamics solution. Most of the future predictions based on these epidemiological models are highly unreliable because of the complexity of the dynamical equations and the poor knowledge of realistic values of the model parameters. The incidence time series of top ten corona infected countries are erratic and sparse. But in comparison, the incidence and disease fitness relationships are uniform and concave upward in nature. These simple profiles with the acceleration curves have fundamental implications in understanding the instinctive dynamics of the corona pandemic. We propose a simple population dynamics solution based on the incidence-fitness relationship in predicting that a plateau or steady state of SARS-CoV-2 will be reached using the basic concept of geometry.

Molecular mechanism of sulfur chemolithotrophy in the betaproteobacterium Pusillimonas ginsengisoli SBSA.

Chemolithotrophic sulfur oxidation represents a significant part of the biogeochemical cycling of this element. Due to its long evolutionary history, this ancient metabolism is well known for its extensive mechanistic and phylogenetic diversification across a diverse taxonomic spectrum. Here we carried out whole-genome sequencing and analysis of a new betaproteobacterial isolate, Pusillimonas ginsengisoli SBSA, which is found to oxidize thiosulfate via the formation of tetrathionate as an intermediate. The 4.7 Mb SBSA genome was found to encompass a soxCDYZAXOB operon, plus single thiosulfate dehydrogenase (tsdA) and sulfite : acceptor oxidoreductase (sorAB) genes. Recombination-based knockout of tsdA revealed that the entire thiosulfate is first converted to tetrathionate by the activity of thiosulfate dehydrogenase (TsdA) and the Sox pathway is not functional in this bacterium despite the presence of all necessary sox genes. The ∆soxYZ and ∆soxXA knockout mutants exhibited a wild-type-like phenotype for thiosulfate/tetrathionate oxidation, whereas ∆soxB, ∆soxCD and soxO::KanR mutants only oxidized thiosulfate up to tetrathionate intermediate and had complete impairment in tetrathionate oxidation. The substrate-dependent O2 consumption rate of whole cells and the sulfur-oxidizing enzyme activities of cell-free extracts, measured in the presence/absence of thiol inhibitors/glutathione, indicated that glutathione plays a key role in SBSA tetrathionate oxidation. The present findings collectively indicate that the potential glutathione : tetrathionate coupling in P. ginsengisoli involves a novel enzymatic component, which is different from the dual-functional thiol dehydrotransferase (ThdT), while subsequent oxidation of the sulfur intermediates produced (e.g. glutathione : sulfodisulfane molecules) may proceed via the iterative action of soxBCD .

An extended stochastic Allee model with harvesting and the risk of extinction of the herring population.

Overexploitation of commercially beneficial fish is a serious ecological problem around the world. The growth profiles of most of the species are likely to follow density regulated theta-logistic model irrespective of any taxonomy group [Sibly et al., Science, 2005]. Rapid depletion of population size may cause reduced fitness, and the species is exposed to Allee phenomena. Here sustainability is addressed by modelling the herring population as a stochastic process and computing the probability of extinction and expected time to extinction. The models incorporate an Allee effect, crowding effect which reduce birth and death rates at large populations, and two possible choices of harvesting models viz. linear harvesting and nonlinear harvesting. A seminal attempt is made by Saha [Saha et al., Ecol. Model, 2013] for this economically beneficial fish, but ignored the vital phenomena of harvesting. Moreover, in this model, the demographic stochasticity is introduced through the white-noise term, which has certain limitations when harvesting is introduced into the system. White noise is appropriate for such a system where immigration and emigration are allowed, but a harvesting model is rational for a closed system. The demographic stochasticity is introduced by replacing an ordinary differential equation model with a stochastic differential equation model, where the instantaneous variance in the SDE is derived directly from the birth and death rates of a birth-death process. The modelling parameters are fit to data of the herring populations collected from Global Population Dynamics Database (GPDD), and the risk of extinction of each population is computed under different harvesting protocols. A threshold for handling times is computed beneath which the risk of extinction is high. This is proposed as a recommendation to management for sustainable harvesting.

A Novel Unification Method to Characterize a Broad Class of Growth Curve Models Using Relative Growth Rate.

Growth curve models serve as the mathematical framework for the quantitative studies of growth in many areas of applied science. The evolution of novel growth curves can be categorized in two notable directions, namely generalization and unification. In case of generalization, a modeler starts with a simple mathematical form to describe the behavior of the data and increases the complexity of the equation by incorporating more parameters to obtain a more flexible shape. The unification refers to the process of obtaining a compact representation of a large number of growth equations. An enormous number of growth equations are made available in the literature by means of the generalization of existing growth laws. However, the unification of growth equations has received relatively less attention from the researchers. Two significant unification functions are available in the literature, namely the Box-Cox transformation by Garcia (For Biometry Model Inf Sci 1:63-68, 2005) and generalized logarithmic and exponential functions by Martinez et al. (Phys A 387:5679-5687, 2008; Phys A 388:2922-2930, 2009). Existing unification approaches are found to have limited applications if the growth equation is characterized by the relative growth rate (RGR). RGR has immense practical value in biological growth curve analysis, which has been amplified by the construction of size and time covariate models, in which; RGR is represented either as a function of size or time or both. The present study offers a unification function for the RGR growth curves. The proposed function combines a broad class of the growth curves and possesses a greater generality than the existing unification functions. We also propose the notion of generalized RGR, which is capable of making interrelations among the unifying functions. Our proposed method is expected to enhance the generality of software and may aid in choosing an optimal model from a set of competitor growth equations.

Evolution of model specific relative growth rate: Its genesis and performance over Fisher's growth rates.

Growth curve models play an instrumental role to quantify the growth of biological processes and have immense practical applications across disciplines. In the modelling approach, the absolute growth rate and relative growth rate (RGR) are two most commonly used measures of growth rates. RGR is empirically estimated by Fisher (1921) assuming exponential growth between two consecutive time points and remains invariant under any choice of the underlying growth model. In this article, we propose a new measure of RGR, called modified RGR, which is sensitive to the choice of underlying growth law. The mathematical form of the growth equations are utilized to develop the formula for model dependent growth rates and can be easily computed for commonly used growth models. We compare the efficiency of Fisher's measure of RGR and modified RGR to infer the true growth profile. To achieve this, we develop a goodness of fit testing procedure using Gompertz model as a test bed. The relative efficiency of the two rate measures is compared by generating power curves of the goodness of fit testing procedure. The asymptotic distributions of the associated test statistics are elaborately studied under Gompertz set up. The simulation experiment shows that the proposed formula has better discriminatory power than the existing one in identifying the true profile. The claim is also verified using existing real data set on fish growth. An algorithm for the model selection mechanism is also proposed based on the modified RGR and is generalized for some commonly used other growth models. The proposed methodology may serve as a valuable tool in growth studies in different research areas.

Protein tyrosine phosphatase 1B is a key regulator of IFNAR1 endocytosis and a target for antiviral therapies.

Type 1 interferons (IFN1) elicit antiviral defenses by activating the cognate receptor composed of IFN-α/ß receptor chain 1 (IFNAR1) and IFNAR2. Down-regulation of this receptor occurs through IFN1-stimulated IFNAR1 ubiquitination, which exposes a Y466-based linear endocytic motif within IFNAR1 to recruitment of the adaptin protein-2 complex (AP2) and ensuing receptor endocytosis. Paradoxically, IFN1-induced Janus kinase-mediated phosphorylation of Y466 is expected to decrease its affinity for AP2 and to inhibit the endocytic rate. To explain how IFN1 promotes Y466 phosphorylation yet stimulates IFNAR1 internalization, we proposed that the activity of a protein tyrosine phosphatase (PTP) is required to enable both events by dephosphorylating Y466. An RNAi-based screen identified PTP1B as a specific regulator of IFNAR1 endocytosis stimulated by IFN1, but not by ligand-independent inducers of IFNAR1 ubiquitination. PTP1B is a promising target for treatment of obesity and diabetes; numerous research programs are aimed at identification and characterization of clinically relevant inhibitors of PTP1B. PTP1B is capable of binding and dephosphorylating IFNAR1. Genetic or pharmacologic modulation of PTP1B activity regulated IFN1 signaling in a manner dependent on the integrity of Y466 within IFNAR1 in human cells. These effects were less evident in mouse cells whose IFNAR1 lacks an analogous motif. PTP1B inhibitors robustly augmented the antiviral effects of IFN1 against vesicular stomatitis and hepatitis C viruses in human cells and proved beneficial in feline stomatitis patients. The clinical significance of these findings in the context of using PTP1B inhibitors to increase the therapeutic efficacy of IFN against viral infections is discussed.

Simultaneous identification of growth law and estimation of its rate parameter for biological growth data: a new approach.

Scientific formalizations of the notion of growth and measurement of the rate of growth in living organisms are age-old problems. The most frequently used metric, "Average Relative Growth Rate" is invariant under the choice of the underlying growth model. Theoretically, the estimated rate parameter and relative growth rate remain constant for all mutually exclusive and exhaustive time intervals if the underlying law is exponential but not for other common growth laws (e.g., logistic, Gompertz, power, general logistic). We propose a new growth metric specific to a particular growth law and show that it is capable of identifying the underlying growth model. The metric remains constant over different time intervals if the underlying law is true, while the extent of its variation reflects the departure of the assumed model from the true one. We propose a new estimator of the relative growth rate, which is more sensitive to the true underlying model than the existing one. The advantage of using this is that it can detect crucial intervals where the growth process is erratic and unusual. It may help experimental scientists to study more closely the effect of the parameters responsible for the growth of the organism/population under study.

Interaction intensity in strategic fitness: A quantifying yardstick of selection optimization for evolutionary game.

The notion of the fitness of a strategy has been assimilated as the reproductive success in the evolutionary game. Initially, this fitness was tied to the game's pay-off and the strategy's relative frequency. However, density dependence becomes exigent in order to make ecologically reliable fitness. However, the contributions of each different type of interaction to the species's overall growth process were surprisingly under-explored. This oversight has occasionally led to either more or less prediction of strategy selection compared to the actual possibility. Moreover, density regulation of the population has always been analysed in a general way compared to strategy selection. In this context, our study introduces the concept of mean relative death payoff, which helps in assessing interaction intensity coefficients and integrates them into strategic fitness. Based on this fitness function, we develop the frequency-density replicator dynamics, which eventually provides distinguishing criteria for directional and balancing selection. Our optimized, evolutionarily stable strategy emerges as a superior alternative to the conventional trade-off between selection forces and ecological processes. More significantly, mean relative death pay-off has both conditional and quantitative roles in getting a stable population size. As a case study, we have extensively analysed the evolution of aggression using the Hawk-Dove game. We have shown that pure Dove selection is always beneficial for species growth rather than pure Hawk selection, and the condition of selection is dependent on external mortality pressure. However, the condition of coexistence is independent of external mortality pressure, representing a strong evolutionary selection that optimizes population density governed by interaction intensity.

Evaluating the efficacy of vermicomposted products in rain-fed wetland rice and predicting potential hazards from metal-contaminated tannery sludge using novel machine learning tactic.

The study assessed the ecotoxicity and bioavailability of potential metals (PMs) from tannery waste sludge, alongside addressing the environmental concerns of overuse of chemical fertilizers, by comparing the impacts of organic vermicomposted tannery waste, chemical fertilizers, and sole application of tannery waste on soil and rice (Oryza sativa L.) plants. The results revealed that T3, which received high-quality vermicomposted tannery waste as an amendment, exhibited superior enzymatic characteristics compared to tannery sludge amended (TWS) treatments (T8, T9). After harvesting, vermicomposted tannery waste treatment (T3) showed a more significant decrease in PMs bioavailability. Accumulation of PMs in rice was minimal across all treatments except T8 and T9, where toxic tannery waste was present, resulting in a high-risk classification (class 5 < 0.01) according to the SAMOE risk assessment. Results from Fuzzy-TOPSIS, ANN, and Sobol sensitivity analyses (SSA) further indicated that elevated concentrations of PMs (Ni, Pb, Cr, Cu) adversely impacted soil-plant health synergy, with T3 showing a minimal risk in comparison to T8 and T9. According to SSA, microbial biomass carbon and acid phosphatase activity were the most sensitive factors affected by PMs concentrations in TWS. The results from the ANN assay revealed that the primary contributing factor of toxicity on the TWS was the exchangeable fraction of Cr. Correlation statistics underscored the significant detrimental effect of PMs' bioavailability on microbial and enzymatic parameters. Overall, the findings suggest that vermicomposting of tannery sludge waste shows potential as a viable organic amendment option in the near future.

Impact of isotype on the mechanism of action of agonist anti-OX40 antibodies in cancer: implications for therapeutic combinations.

BACKGROUND: OX40 has been widely studied as a target for immunotherapy with agonist antibodies taken forward into clinical trials for cancer where they are yet to show substantial efficacy. Here, we investigated potential mechanisms of action of anti-mouse (m) OX40 and anti-human (h) OX40 antibodies, including a clinically relevant monoclonal antibody (mAb) (GSK3174998) and evaluated how isotype can alter those mechanisms with the aim to develop improved antibodies for use in rational combination treatments for cancer. METHODS: Anti-mOX40 and anti-hOX40 mAbs were evaluated in a number of in vivo models, including an OT-I adoptive transfer immunization model in hOX40 knock-in (KI) mice and syngeneic tumor models. The impact of FcγR engagement was evaluated in hOX40 KI mice deficient for Fc gamma receptors (FcγR). Additionally, combination studies using anti-mouse programmed cell death protein-1 (mPD-1) were assessed. In vitro experiments using peripheral blood mononuclear cells (PBMCs) examining possible anti-hOX40 mAb mechanisms of action were also performed. RESULTS: Isotype variants of the clinically relevant mAb GSK3174998 showed immunomodulatory effects that differed in mechanism; mIgG1 mediated direct T-cell agonism while mIgG2a acted indirectly, likely through depletion of regulatory T cells (Tregs) via activating FcγRs. In both the OT-I and EG.7-OVA models, hIgG1 was the most effective human isotype, capable of acting both directly and through Treg depletion. The anti-hOX40 hIgG1 synergized with anti-mPD-1 to improve therapeutic outcomes in the EG.7-OVA model. Finally, in vitro assays with human peripheral blood mononuclear cells (hPBMCs), anti-hOX40 hIgG1 also showed the potential for T-cell stimulation and Treg depletion. CONCLUSIONS: These findings underline the importance of understanding the role of isotype in the mechanism of action of therapeutic mAbs. As an hIgG1, the anti-hOX40 mAb can elicit multiple mechanisms of action that could aid or hinder therapeutic outcomes, dependent on the microenvironment. This should be considered when designing potential combinatorial partners and their FcγR requirements to achieve maximal benefit and improvement of patient outcomes.

Bcr-abl signals to desensitize chronic myeloid leukemia cells to IFNα via accelerating the degradation of its receptor.

Constitutive activity of Bcr-abl fusion protein kinase causes chronic myeloid leukemia (CML). Inhibitors of Bcr-abl such as imatinib mesylate have replaced the cytokine IFNα as the primary treatment for the management of patients with this malignancy. We found that pretreatment of CML cells with imatinib mesylate augments the antigrowth effects of IFNα. Furthermore, introduction of Bcr-abl into non-CML cells inhibits the cellular responses to IFNα. This inhibition is mediated via a mechanism that involves activation of protein kinase D2. The latter promotes an accelerated phosphorylation-dependent degradation of the interferon-α/ß receptor 1 chain of the type I interferon receptor, leading to attenuation of IFNα signaling. We discuss the relationship between Bcr-abl activity and IFNα signaling as a molecular basis of the combination of inhibitors of Bcr-abl and IFNα for CML treatment.

Paradox of enrichment: A fractional differential approach with memory.

The paradox of enrichment (PoE) proposed by Rosenzweig [M. Rosenzweig, The paradox of enrichment, Science 171 (1971) 385-387] is still a fundamental problem in ecology. Most of the solutions have been proposed at an individual species level of organization and solutions at community level are lacking. Knowledge of how learning and memory modify behavioral responses to species is a key factor in making a crucial link between species and community levels. PoE resolution via these two organizational levels can be interpreted as a microscopic- and macroscopic-level solution. Fractional derivatives provide an excellent tool for describing this memory and the hereditary properties of various materials and processes. The derivatives can be physically interpreted via two time scales that are considered simultaneously: the ideal, equably flowing homogeneous local time, and the cosmic (inhomogeneous) non-local time. Several mechanisms and theories have been proposed to resolve the PoE problem, but a universally accepted theory is still lacking because most studies have focused on local effects and ignored non-local effects, which capture memory. Here we formulate the fractional counterpart of the Rosenzweig model and analyze the stability behavior of a system. We conclude that there is a threshold for the memory effect parameter beyond which the Rosenzweig model is stable and may be used as a potential agent to resolve PoE from a new perspective via fractional differential equations.

Probiotic Characterization of Arsenic-resistant Lactic Acid Bacteria for Possible Application as Arsenic Bioremediation Tool in Fish for Safe Fish Food Production.

Arsenic (As) contamination of water and food is a global problem posing a severe threat to environmental and human health; therefore, fish as an aquatic animal is immensely affected by the hazardous impacts of As. The present study aimed to explore the As-resistant probiotic bacteria and characterize their potential for applying as an As bioremediation tool in fish. As-resistant lactic acid bacteria (LAB) were isolated from sludge samples of an old stabilization pond/lagoon of wastewater treatment plant using spared plate techniques. The potential probiotic was selected by assessing the sequential probiotic characterization, As resistance and removal properties. The selected probiotic was identified by PCR-based molecular method using 16S rDNA. A total of 51 As-resistant LAB were isolated from sludge samples. Potential six As-resistant LAB strains (As4, 11, 20, 21, 41 and 48) were selected from 51 isolates through sequential probiotic characterizations using mimic fish gastrointestinal conditions. The selected probiotics displayed relatively elevated As (> 1000 mg L-1), cadmium (20-100 mg L-1) and lead (> 2000 mg L-1)-resistant patterns and excellent As-removal efficiencies (0.0012-0.0044 mg As mg cell-1 h-1) from water along with favourable various associative probiotic properties. The 16S rDNA sequence-based molecular identification and phylogenetic analysis revealed that the strains As4, 11, 20, 21, 41 and 48 belong to Limosilactobacillus fermentum (Lactobacillus fermentum according to old taxonomy). The As removal and survival in mimic gastrointestinal conditions of fish indicated that new Limosilactobacillus fermentum strains could be employed as the novel and potential probiotic tools for possible bioremediation of As and other pollutants in the fish to prevent the bioaccumulation and toxicity impacts of As in fish for cleaner and safe fish food production.

Role of p38 protein kinase in the ligand-independent ubiquitination and down-regulation of the IFNAR1 chain of type I interferon receptor.

Phosphorylation-dependent ubiquitination and degradation of the IFNAR1 chain of type I interferon (IFN) receptor is a robust and specific mechanism that limits the magnitude and duration of IFNα/ß signaling. Besides the ligand-inducible IFNAR1 degradation, the existence of an "inside-out" signaling that accelerates IFNAR1 turnover in the cells undergoing the endoplasmic reticulum (ER) stress and activated unfolded protein responses has been recently described. The latter pathway does not require either presence of ligands (IFNα/ß) or catalytic activity of Janus kinases (JAK). Instead, this pathway relies on activation of the PKR-like ER kinase (PERK) and ensuing specific priming phosphorylation of IFNAR1. Here, we describe studies that identify the stress activated p38 protein kinase as an important regulator of IFNAR1 that acts downstream of PERK. Results of the experiments using pharmacologic p38 kinase inhibitors, RNA interference approach, and cells from p38α knock-out mice suggest that p38 kinase activity is required for priming phosphorylation of IFNAR1 in cells undergoing unfolded protein response. We further demonstrate an important role of p38 kinase in the ligand-independent stimulation of IFNAR1 ubiquitination and degradation and ensuing attenuation of IFNα/ß signaling and anti-viral defenses. We discuss the distinct importance of p38 kinase in regulating the overall responses to type I IFN in cells that have been already exposed to IFNα/ß versus those cells that are yet to encounter these cytokines.

A noble extended stochastic logistic model for cell proliferation with density-dependent parameters.

Cell proliferation often experiences a density-dependent intrinsic proliferation rate (IPR) and negative feedback from growth-inhibiting molecules in culture media. The lack of flexible models with explanatory parameters fails to capture such a proliferation mechanism. We propose an extended logistic growth law with the density-dependent IPR and additional negative feedback. The extended parameters of the proposed model can be interpreted as density-dependent cell-cell cooperation and negative feedback on cell proliferation. Moreover, we incorporate further density regulation for flexibility in the model through environmental resistance on cells. The proposed growth law has similarities with the strong Allee model and harvesting phenomenon. We also develop the stochastic analog of the deterministic model by representing possible heterogeneity in growth-inhibiting molecules and environmental perturbation of the culture setup as correlated multiplicative and additive noises. The model provides a conditional maximum sustainable stable cell density (MSSCD) and a new fitness measure for proliferative cells. The proposed model shows superiority to the logistic law after fitting to real cell culture datasets. We illustrate both conditional MSSCD and the new cell fitness for a range of parameters. The cell density distributions reveal the chance of overproliferation, underproliferation, or decay for different parameter sets under the deterministic and stochastic setups.