Experimental Design Strategy Research Articles

Analytical measuring interval, linearity, and precision of serology assays for detection of SARS-CoV-2 antibodies according to CLSI guidelines.

Reliable and validated serology assays are of increasing importance as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus continues to evolve and cause outbreaks. Validation of serology assays along with calibration to the International and National Standards (such as anti-SARS-CoV-2 Immunoglobulin WHO International Standard 20/136 or Frederick National Laboratory for Cancer Research's National Serology Standard COVID-NS01097) is critical to ensuring that results from clinical studies are reliable and comparable among various assays and laboratories. We describe the design and execution of a comprehensive study that established the analytical measuring intervals, linearity, precision, and repeatability of four in-house developed serology enzyme-linked immunosorbent assays (SARS-CoV-2 anti-Spike immunoglobin G [IgG] and immunoglobin M [IgM] and anti-Nucleocapsid IgG and IgM) following applicable Clinical and Laboratory Standards Institute (CLSI) guidelines. Overall, this study provides practical guidance on experimental design strategies and data analysis techniques, pertaining to the validation of COVID-19 serology assays according to CLSI guidelines, for use in clinical research studies.

Megamolecule Self-Assembly Networks: A Combined Computational and Experimental Design Strategy.

This work describes the use of computational strategies to design megamolecule building blocks for the self-assembly of lattice networks. The megamolecules are prepared by attaching four Cutinase-SnapTag fusion proteins (CS fusions) to a four-armed linker, followed by functionalizing each fusion with a terpyridine linker. This functionality is designed to participate in a metal-mediated self-assembly process to give networks. This article describes a simulation-guided strategy for the design of megamolecules to optimize the peptide linker in the fusion protein to give conformations that are best suited for self-assembly and therefore streamlines the typically time-consuming and labor-intensive experimental process. We designed 11 candidate megamolecules and identified the most promising linker, (EAAAK)2, along with the optimal experimental conditions through a combination of all-atom molecular dynamics, enhanced sampling, and larger-scale coarse-grained molecular dynamics simulations. Our simulation findings were validated and found to be consistent with the experimental results. Significantly, this study offers valuable insight into the self-assembly of megamolecule networks and provides a novel and general strategy for large biomolecular material designs by using systematic bottom-up coarse-grained simulations.

Experimenting innovative active sound design to reduce driver weariness in electric vehicles

With the current electrification trend in the automotive industry, electric vehicle sonification, both interior and exterior alike, has become common practice. To provide drivers with an alternative to the familiar thermal engine rumble, car manufacturers have been exploring many sound design strategies. Quite often however, these designs suffer from a form of weariness that the driver may feel over time, many of them eventually choosing to simply turn off these sounds meant to help them. The goal of the present work is to address this issue by proposing some experimental active sound design strategies whose goal is to break the relative monotony of classical electric vehicle sound design. This is achieved thanks to the combination of advanced seamless audio synthesis tools and generic preprocessing of vehicle data to create new audio control parameters allowing us to explore uncharted grounds for electric vehicle active sound design.

Research progress on single-cell expression quantitative trait loci.

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.

Study design: think 'scientific value' not 'p-values'.

Statistically based experimental designs have been available for over a century. However, many preclinical researchers are completely unaware of these methods, and the success of experiments is usually equated only with 'p < 0.05'. By contrast, a well-thought-out experimental design strategy provides data with evidentiary and scientific value. A value-based strategy requires implementation of statistical design principles coupled with basic project management techniques. This article outlines the three phases of a value-based design strategy: proper framing of the research question, statistically based operationalisation through careful selection and structuring of appropriate inputs, and incorporation of methods that minimise bias and process variation. Appropriate study design increases study validity and the evidentiary strength of the results, reduces animal numbers, and reduces waste from noninformative experiments. Statistically based experimental design is thus a key component of the 'Reduction' pillar of the 3R (Replacement, Reduction, Refinement) principles for ethical animal research.

Data Processing for Predicting DNA Damaging Properties of Complex UV Sources.

A growing number of experimental evidence emphasizes that photobiological phenomena are not always the sum of the effect of individual wavelengths present in the emission spectrum of light sources. Unfortunately, tools are missing to identify such non-additive effects and predict effects of various exposure conditions. In the present work, we addressed these points for the formation of pyrimidine dimers in DNA upon co-exposure to UVC, UVB and UVA radiation. We first applied a combination index approach to determine whether mixtures of theses UV ranges exhibited additive, inhibitory or synergistic effects on the formation of cyclobutane pyrimidine dimers, (6-4) photoproducts and Dewar valence isomers. A predictive approach based on an experimental design strategy was then used to quantify the contribution of each wavelength range to the formation of DNA photoproducts. The obtained models allowed us to accurately predict the level of pyrimidine dimers in DNA irradiated under different conditions. The data were found to be more accurate than those obtained with the simple additive approach underlying the use of action spectra. Experimental design thus appears as an attractive concept that could be widely applied in photobiology even for cellular experiments.

Thiolated nanoclays as a potential mucoadhesive material: Optimization by design of experiments and multivariate regression

An integrated strategy based on design of experiments (DoE) and multivariate regression was carried out to produce an optimized surface functionalized nanoclay (Mt–SH) with potential mucoadhesive properties. A mercaptosilane was grafted in montmorillonite in order to immobilize thiol groups (–SH) on the clay. The functionalization was confirmed by IR, TGA, XRF and XRD analyses. Five variables involved in the synthesis were optimized in order to maximize two responses: mass of functionalized nanoclay and amount of thiol groups attached in the clay surface. This optimization was performed by a two–step DoE strategy using Plackett–Burman and Box–Behnken designs. The five variables and two responses were simultaneously calibrated in a single ANOVA–validated PLS regression model. Analyzing scores, loading and surface response plots, the main processing variables, interactions and quadratic terms were found achieving the optimal processing conditions for the synthesis. The quadratic model (R2 > 0.8589) predicted an optimized functionalized nanoclay of mass 3.29 ± 0.13 g of Mt–SH with 19.65 ± 0.78 μmol –SH per g Mt–SH (P < 0.05). The optimized Mt–SH was evaluated by in vitro and in situ mucoadhesion assays with porcine mucin and fresh intestinal mucus by using rheometry. The complex viscosity showed an increase of ∼54-fold higher compared to the unmodified Mt, and ∼12.8-fold increase in the elastic modulus, which indicates a more resistance to elastic deformation probably due to an increase in cross-linking points between Mt–SH and mucus. The formation of disulfide bonds between thiol groups from Mt–SH and glycoproteins in the mucin could be a reasonably explanation which shows a correlation respect other thiolated materials which show the same mucoadhesion properties. These results could project this functionalized silicate material as a potential mucoadhesive prospect for pharmaceutical applications.

Experimental Designs in Management Studies for Bias Assessment: Setting and Stimulus Material

This manuscript stems from an ongoing research project on gender inequality in Portuguese businesses, particularly concerning access to leadership positions. It aims to provide new insights into the constraints that limit women’s access to managerial roles – both from the worker/employee’s perspective, by exploring expectations, aspirations, and perceived barriers, and from the employer/hierarchy’s perspective, by analysing which biases in recruitment and promotion processes are likely to limit women’s rise to top management positions. Against this analytical background, this paper aims to provide a reflection on the inquiry protocol developed within the project, with particular emphasis on the quantitative approach based on an experimental design. A survey has been developed targeting professionals and directors of large, listed companies and large/medium unlisted companies, where the respondents are given the framework of hiring hypothetical applicants for a management position. The methodological protocol involves elaborating different fictitious CV profiles, representing hypothetical candidates. These profiles are constructed by varying attributes according to various dimensions, specifically gender, combined with educational and professional characteristics. Based on the process of designing the experimental setting, the aim is to reflect on the nuances, obstacles, and limitations in constructing a set of stimulus materials and manipulating the experimental conditions regarding the fictitious applicants’ characteristics (including gender), vertical segregation (‘glass ceiling’ in access to top management positions), horizontal segregation (i.e., associating candidates with typically female or male occupations) and measurement items/scales for assessing the applicants in different dimensions (e.g., perceived competence, job fit, promotion recommendation, etc.). Therefore, this paper aims to reflect on the methodological challenges and advantages of an experimental research design and data analysis strategy to address gender bias in hiring contexts and capable of sustaining an ongoing data observatory to support the definition of public policies and organisational practices aimed at promoting gender equality in top positions.

Fine particle capture performance assessment in an electrostatic-fabric integrated precipitator

Electrostatic precipitators, which are more commonly referred to as precipitators, play a crucial role in the process of reducing the quantity of airborne contaminants that are present in both atmospheric and flue gas settings. This is because precipitators are able to lower the concentration of contaminants in the air. The fine particles, ash, and oil that make up these pollutants are included. With a special emphasis on a Cottrell-type electro-filter that makes use of the wire-cylinder arrangement, the goals of this study are to evaluate the practicability of various filtration systems for the purpose of air decontamination. Specifically, the study will focus on the Cottrell-type electro-filter. By conducting a thorough examination of the peculiarities of the electrical and physical parameters that regulate the mechanisms of particle aggregation, our major purpose is to achieve a comprehensive understanding of these parameters. Through the use of experimental design strategies, we will make an effort to construct and perfect the filtration process in the subsequent stage. In addition to that, the purpose of this inquiry is to explore the impact that high voltage levels and the diameter of the conductor wire that is incorporated into the prototype have. Especially notable is the fact that our prototype exhibits an incredible filtration efficiency, with rates reaching up to 98%. This performance is really remarkable. Its exceptional performance reveals that it has the potential to be suitable for a wide variety of contexts, including residential, commercial, healthcare, industrial, and workshop applications. This suggests that it has the potential to be suitable for a wide range of applications.

Automated optimal experimental design strategy for reduced order modeling of aerodynamic flow fields

Aerodynamic flow fields reveal essential physical insights (such as shocks) that substantially affect aerodynamic performance. However, conventional flow field computations require time-consuming simulations. Alternatively, reduced-order models (ROMs) allow fast flow field predictions, enabling real-time decision-making. Efficient sampling is required to train ROMs without incuring prohibitive costs. In this paper, we propose a fully automated optimal experimental design (Auto-OED) strategy on proper orthogonal decomposition (POD) for ROM-based rapid flow field predictions. Auto-OED uses two individual optimal experimental design (OED) strategies, automatically selects the number of POD bases for the first sampling strategy, and intelligently switches to the second strategy on the fly. We showcased the Auto-OED strategy on airfoil flow field predictions in the transonic regime. OED-based ROMs were constructed over 20 trials for robustness tests with a total of 40 training samples in each trial, including 16 random initial samples by Latin hypercube sampling (LHS) and 24 OED samples. The results demonstrated that the ROM predictions completely based on LHS had an error of 1.6×10−3 while the existing OED strategies-based ROMs over 20 trials achieved a mean error (μerr) of 7.5×10−4 with a standard deviation (σerr) of 9.0×10−5. In contrast, the best Auto-OED ROM achieved the lowest μerr of 7.5×10−4 and lowest σerr of 6.2×10−5. These error reductions confirm the viability of the proposed Auto-OED-based ROM in fluid field predictions and potentially other engineering applications of a similar type.

Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review.

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.

Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method.

Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25(56). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.

Simulation Experiment Design and Control Strategy Analysis in Teaching of Hydrogen-Electric Coupling System

Hydrogen energy, as a clean and green energy medium, is characterized by large capacity, extended lifespan, convenient storage, and seamless transmission. On the one hand, in the power system, hydrogen can be prepared by the electrolysis of water using the surplus power from intermittent new energy generation, such as photovoltaic and wind power, to increase the space for new energy consumption. On the other hand, it can be used to generate electricity from the chemical reaction between hydrogen and oxygen through the fuel cell and be used as a backup power source when there is a shortage of power supply. In this paper, based on the teaching practice, the conversion mechanism and coupling relationship between various forms of energy, such as photovoltaic energy, hydrogen energy, and electric energy, were deeply analyzed. Further, a hydrogen-electricity coupling digital simulation experimental system, including photovoltaic power generation, fuel cell, and electrolysis hydrogen system, was formed. Simultaneously, considering the synergy between hydrogen production and electricity generation businesses, as well as the demand for the efficient utilization and flexible regulation of multiple energy sources, eight sets of simulation experimental scenarios were designed. A cooperative control strategy for the hydrogen-electric coupling system was proposed and validated through simulation on the MATLAB/SIMULINK-R2023a platform. This study shows that the simulation system has rich experimental scenarios and control strategies, and can comprehensively and accurately demonstrate the multi-energy complementary and cooperative control characteristics of the hydrogen-electric coupling system.

Crafting an innovative bimetallic MOF-on-MOF/TiO2 composite for effective removal of Imatinib anticancer agent through adsorption and photodegradation

Imatinib (IMB), a widely employed anticancer agent, poses significant environmental concerns due to its release into aquatic ecosystems via pharmaceutical industries and hospital effluents, contributing to water source contamination and consequential impacts on both human and animal health. In light of this, a novel bimetallic MIL-53(Co-Fe)@MIL-53(Ni)/TiO2 composite material has been synthesized utilizing hydrothermal approaches for the efficient adsorption and photodegradation of IMB. Comparative assessments were conducted between this composite and MIL-53(Ni), bimetallic MIL-53(Co-Fe)@MIL-53(Ni), bare TiO2 for their respective capacities in IMB removal through adsorption and photodegradation. Remarkably enhanced efficacy was observed in the synthetic composite's performance for both adsorption and photodegradation, surpassing that of other compounds. Through systematic exploration, critical parameters influencing the adsorption and photodegradation processes were meticulously examined and subsequently optimized utilizing experimental design strategies. Optimal conditions led to the identification of a pseudo-second-order kinetic model for IMB adsorption with a notable rate constant of 8.39 and an equilibrium time of 75 min. The adsorption process conformed favorably to the Freundlich isotherm, as evidenced by the Freundlich constant of 25.498 mg g−1 and adsorption intensity of 2.09. These values underscored the heightened affinity of the synthetic composite relative to IMB, ascribed to the favorable absorption. A complementary investigation into the kinetics of IMB photodegradation unveiled a substantially elevated rate constant of 0.0407 min−1, coupled with a proper half-life of 17.03 min. These collective findings validate the exceptional capacity of the synthesized composite for dual-mode IMB elimination, encompassing both adsorption and photodegradation pathways. This advancement holds significant promise for addressing the environmental repercussions of IMB contamination and points to the potential for broader applications in wastewater treatment and ecological restoration endeavors.

A review on computer model calibration

AbstractModel calibration is crucial for optimizing the performance of complex computer models across various disciplines. In the era of Industry 4.0, symbolizing rapid technological advancement through the integration of advanced digital technologies into industrial processes, model calibration plays a key role in advancing digital twin technology, ensuring alignment between digital representations and real‐world systems. This comprehensive review focuses on the Kennedy and O'Hagan (KOH) framework (Kennedy and O'Hagan, Journal of the Royal Statistical Society: Series B 2001; 63(3):425–464). In particular, we explore recent advancements addressing the challenges of the unidentifiability issue while accommodating model inadequacy within the KOH framework. In addition, we explore recent advancements in adapting the KOH framework to complex scenarios, including those involving multivariate outputs and functional calibration parameters. We also delve into experimental design strategies tailored to the unique demands of model calibration. By offering a comprehensive analysis of the KOH approach and its diverse applications, this review serves as a valuable resource for researchers and practitioners aiming to enhance the accuracy and reliability of their computer models.This article is categorized under:Statistical Models &gt; Semiparametric ModelsStatistical Models &gt; Simulation ModelsStatistical Models &gt; Bayesian Models

Parametric Optimizing Green Sand-Casting Process Parameters using hybrid Taguchi Grey Relational Analyses and Principal Component Analyses

The Green Sand-casting technique is a very ancient method of casting that has many different uses. The increased rate of errors and rejection in this process is a key drawback that reduces output and profits. It’s challenging to develop a good link between the many different parameters and defects since the process is so complicated. This article describes a hybrid approach to find the co-relation for sand casting process’s variables. This approach mixes the Taguchi method (TM) with Grey Relational Analysis (GRA) paired with Principal Component Analysis (PCA). Moisture content, Permeability, Loss of Ignition, Pouring Time &amp; Pouring Temperature selected as input parameters while types of defects (Shrinkage, Blow holes, Cracks, Porosity) as responses for proposed study. The L27 OA from Taguchi is used to plan the tests. TM implemented to analyse individual responses. GRA is applied to find optimal solutions for a set of replies, whereas PCA is used to determine how much weight each response should be given. Using proposed methodology, 4% moisture content, 160% permeability, 5% loss of ignition, 60 seconds of pouring time, and 1400°C found as optimum set of parameters. The findings demonstrate that the hybrid approach, which makes use of both a cost-effective and efficient experimental design strategy, was successful in resolving the complexity trade-off experienced throughout the judgment process of multi-response optimization.

A new spiral press filter to purify tomato juice: A central composite strategy for experimental design and optimization

We investigated and optimized the simultaneous effects of pressure and fluid temperature on the tomato juice filtration process. For this purpose, we manufactured a new spiral press filter. This filter is innovative in that it has a fixed spiral cylinder, while the fluid travels along the spiral path using hydraulic force. This design has the advantages of both press filters and spiral press filters. The turbidity of filtered tomato juice decreased from 1632NTU to 643NTU. The optimum conditions for the tomato juice filtration process were found to be 6 bar fluid pressure and 45 °C fluid temperature. These conditions resulted in a desirability index of 0.99, which is a very good value. The optimum values of the response variables, including permeate flux, total energy consumption, lycopene content, and vitamin C content of filtered tomato juice were 503.15 × 10−6 m3 m2 s−1, 0.033 kWh, 2.44 mg 100 g−1, and 13.40 mg 100 g−1, respectively.

Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) – Science plan and experimental design

The Pliocene Model Intercomparison Project (PlioMIP) was initiated in 2008. Over two phases PlioMIP has helped co-ordinate the experimental design and publication strategy of the community, which has included an increasing number of climate models and modelling groups from around the world. It has engaged with palaeoenvironmental scientists to foster new data synthesis supporting the construction of new model boundary conditions, as well as to facilitate new data-model comparisons. The work has advanced our understanding of Pliocene climates and environments, enhanced our knowledge regarding the ability of complex climate and Earth System models to accurately simulate climate change, and helped to refine our estimates of how sensitive the climate system is to forcing conditions.In this community protocol paper, we outline the scientific plan for PlioMIP Phase 3 (PlioMIP3). This plan provides the required guidance to participating modelling groups from around the world to successfully set up and perform PlioMIP3 climate model experiments. The project is open to new participants from the scientific community (both from the climate modelling and geosciences communities).In PlioMIP3, we retain the PlioMIP2 Core experiments (Eoi400, E280) and extend the Core requirements to include either an experiment focussed on the Early Pliocene or an alternative Late Pliocene simulation (or both). These additions (a) allow a comparison of Early and Late Pliocene warm intervals and help build research connections and synergy with the MioMIP (Miocene Model Intercomparison Project - also known as DeepMIP-Miocene) and PlioMioVAR projects (Pliocene-Miocene Variability Working Group), and (b) create an alternative time slice simulation for 3.205 Ma (MIS KM5c) through removal of some of the largest palaeogeographic differences introduced between PlioMIP1 and 2 resulting in minimal land-sea mask variations from the modern. In addition, we present ten optional experiments designed to enhance our assessment of climate sensitivity and to explore the uncertainty in greenhouse gas-related forcing. For the first time, we introduce orbital sensitivity experiments into the science plan, as well as simulations incorporating dynamic vegetation-climate feedbacks and an experiment designed to examine the potential significance of East Antarctic Ice Sheet boundary condition uncertainty. These changes enhance palaeo-to-future scientific connections and enable an exploration of the significance of palaeogeographic uncertainties on climate simulations.

Bacteria-templated ZIF-8 embedded in polyacrylonitrile nanofibers as a novel sorbent for thin film microextraction of benzoylurea insecticides

In the present work, the rod-like ZIF-8 (ZIF8@E coli) was prepared by fast, easy and environmentally friendly method of biomimetic mineralization with Escherichia coli bacteria as a bio-template and was exploited for the first time in the microextraction. In this regard, electrospun nanofiber mats of polyacrylonitrile (PAN) and ZIF8@E coli were prepared by electrospinning method and used as a new sorbent for thin film microextraction (TFME) of benzoylurea insecticides such as Hexaflumuron and Teflubenzuron as model analytes. The PAN/ZIF8@E coli nanocomposite was characterized using electron scanning microscopy and various spectroscopy techniques. Factors affecting the proposed extraction method were screened and optimized using the experiment design strategy. Then, the model analytes were measured by high-performance liquid chromatography (HPLC) with ultraviolet (UV) detector after microextraction. Satisfactory figures of merit were obtained for suggested TFME-HPLC-UV under optimum conditions. The suitable linearity varied in the range of 0.5–200 μg L−1 with R2 greater than 0.9968. The limit of detections for Hexaflumuron and Teflubenzuron were 0.12 and 0.15 μg L−1, respectively. The application of the method in the real sample was investigated by analyzing the selected analytes in environmental water and food samples. The spiking recovery of the selected analytes varied in the range of 93.0–109.8 % (RSD≤7.68). The results confirm the efficient application of this new sorbent in TFME approach. Considering the high availability, ease of production, and environmental friendliness of bacteria along with the significant improvement of metal-organic framework (MOF) growth efficiency, biomimetic mineralization is expected to be efficient method for the synthesis of ordered MOFs for use in extraction fields.

Hormesis Defines The Limits Of Lifespan.

This commentary provides a novel synthesis of how biological systems adapt to a broad spectrum of environmental and age-related stresses that are underlying causes of numerous degenerative diseases and debilitating effects of aging. It proposes that the most fundamental, evolutionary-based integrative strategy to sustain and protect health is based on the concept of hormesis. This concept integrates anti-oxidant, anti-inflammatory and cellular repair responses at all levels of biological organization (i.e., cell, organ and organism) within the framework of biphasic dose responses that describe the quantitative limits of biological plasticity in all cells and organisms from bacteria andplants to humans. A major feature of the hormetic concept is that low levels of biological, chemical, physical and psychological stress upregulate adaptive responses that not only precondition, repair and restore normal functions to damaged tissues/organs but modestly overcompensate, reducing ongoing background damage, thereby enhancing health beyond that in control groups, lacking the low level "beneficial" stress. Higher doses of such stress often become counterproductive and eventually harmful. Hormesis is active throughout the life-cycle and can be diminished by aging processes affecting the onset and severity of debilitating conditions/diseases, especially in elderly subjects. The most significant feature of the hormetic dose response is that the limits of biological plasticity for adaptive processes are less than twice that of control group responses, with most, at maximum, being 30-60% greater than control group values. Yet, these modest increases can make the difference between health or disease and living or dying. The quantitative features of these adaptive hormetic dose responses are also independent of mechanism. These features of the hormetic dose response determine the capacity to which systems can adapt/be protected, the extent to which biological performance (e.g., memory, resistance to injury/disease, wound healing, hair growth or lifespan) can be enhanced/extended and the extent to which synergistic interactions may occur. Hormesis defines the quantitative rules within which adaptive processes operate and is central to evolution and biology and should become transformational for experimental concepts and study design strategies, public health practices and a vast range of therapeutic strategies and interventions.