Hasil untuk "q-bio"

Francesco Colizzi, Paula Blázquez-Sánchez, Giovanni Bussi et al.

Plastics are essential to modern life, yet poor disposal practices contribute to low recycling rates and environmental accumulation-biological degradation and by-product reuse offer a path to mitigate this global threat. This report highlights key insights, future challenges, and research priorities identified during the CECAM workshop "Computations Meet Experiments to Advance the Enzymatic Depolymerization of Plastics One Atom at a Time", held in Trieste from May 6-8, 2025. The workshop brought together an interdisciplinary community of scientists focused on advancing the sustainable use of plastics through enzyme-based degradation. A key point from the discussions is that many bottlenecks in enzymatic recycling arise not only from process engineering challenges, but also from a limited understanding of the underlying molecular mechanisms. We argue that constraints on economic viability and sustainability (e.g., harsh solvents, high temperatures, substrate crystallinity, pretreatments) can-and should-be addressed directly through enzyme design, provided these factors are understood at the molecular level, in synergy with process optimization. For this, it is essential to rely on the integration of experimental and computational approaches to uncover the molecular and mechanistic basis of enzymatic plastic degradation. We highlight how the small-format structure of the workshop, in line with the usual CECAM format, fostered a collaborative, friendly, and relaxed atmosphere. We hope this report encourages future initiatives and the formation of shared consortia to support an open, collaborative, and bio-based plastic recycling community.

Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska

This study explores how the selection of neuron models and learning rules impacts the classification performance of Spiking Neural Networks (SNNs), with a focus on applications in bio-signal processing. We compare biologically inspired neuron models, including Leaky Integrate-and-Fire (LIF), metaneurons, and probabilistic Levy-Baxter (LB) neurons, across multiple learning rules, including spike-timing-dependent plasticity (STDP), tempotron, and reward-modulated updates. A novel element of this work is the integration of a complexity-based decision mechanism into the evaluation pipeline. Using Lempel-Ziv Complexity (LZC), a measure related to entropy rate, we quantify the structural regularity of spike trains and assess classification outcomes in a consistent and interpretable manner across different SNN configurations. To investigate neural dynamics and assess algorithm performance, we employed synthetic datasets with varying temporal dependencies and stochasticity levels. These included Markov and Poisson processes, well-established models to simulate neuronal spike trains and capture the stochastic firing behavior of biological neurons.Validation of synthetic Poisson and Markov-modeled data reveals clear performance trends: classification accuracy depends on the interaction between neuron model, network size, and learning rule, with the LZC-based evaluation highlighting configurations that remain robust to weak or noisy signals. This work delivers a systematic analysis of how neuron model selection interacts with network parameters and learning strategies, supported by a novel complexity-based evaluation approach that offers a consistent benchmark for SNN performance.

H. Padinhare, C. Yang, D. Tu et al.

Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating principles fundamentally different from the ion signal modulation of biology, traditional Silicon-based neuromorphic implementations have limited bio-integration potential. Here, we report the first organic electrochemical neurons (OECNs) with ion-modulated spiking, based on allprinted complementary organic electrochemical transistors. We demonstrate facile biointegration of OECNs with Venus Flytrap (Dionaea muscipula) to induce lobe closure upon input stimuli. The OECNs can also be integrated with all-printed organic electrochemical synapses (OECSs), exhibiting short-term plasticity with paired-pulse facilitation and longterm plasticity with retention >1000 s, facilitating Hebbian learning. These soft and flexible OECNs operate below 0.6 V and respond to multiple stimuli, defining a new vista for localized artificial neuronal systems possible to integrate with bio-signaling systems of plants, invertebrates, and vertebrates.

Md Sayed Tanveer, Dhruvik Patel, Hunter E. Schweiger et al.

With the recent advancements in artificial intelligence, researchers and industries are deploying gigantic models trained on billions of samples. While training these models consumes a huge amount of energy, human brains produce similar outputs (along with other capabilities) with massively lower data and energy requirements. For this reason, more researchers are increasingly considering alternatives. One of these alternatives is known as synthetic biological intelligence, which involves training \textit{in vitro} neurons for goal-directed tasks. This multidisciplinary field requires knowledge of tissue engineering, bio-materials, digital signal processing, computer programming, neuroscience, and even artificial intelligence. The multidisciplinary requirements make starting synthetic biological intelligence research highly non-trivial and time-consuming. Generally, most labs either specialize in the biological aspects or the computational ones. Here, we propose how a lab focusing on computational aspects, including machine learning and device interfacing, can start working on synthetic biological intelligence, including organoid intelligence. We will also discuss computational aspects, which can be helpful for labs that focus on biological research. To facilitate synthetic biological intelligence research, we will describe such a general process step by step, including risks and precautions that could lead to substantial delay or additional cost.

Nargis Nazir, Z. Badri, N. Bhat et al.

Root rot of pea caused by Fusarium spp. is one of the important diseases of pea (Pisum sativum L.). The causal fungus of the disease isolated from naturally infected pea plants was identified as Fusarium solani f. sp. pisi (Jones). Evaluation of four bio agents and nine fungicides was done in vitro against Fusarium solani. Trichoderma harzianum was the most effective bio agent in inhibiting the mycelial growth of F. solani by (82.62%). Carbendazim 50 WP was the most effective fungicide in inhibiting the mycelial growth of F. solani by (91.06%). Carbendazim at the rate of 0.1% and T. harzianum at concentration of 109 cfu when used as seed treatment under field conditions were evaluated along with three planting techniques v.i.z, raised beds, ridges and flat beds. It was found that Carbendazim at the rate of 0.1% when given as seed treatment in raised beds exhibited the lowest disease incidence (10.97%), intensity (2.89%) and the maximum pod yield (89.63 q ha−1) as compared to control.

Chan Sam Park, Hyunjoong Kim, Kwangmuk Lee et al.

Recent wearable blood pressure (BP) measurements that are based on the pulse transit time (PTT) require additional bothersome contact for their subordinate electrocardiogram (ECG), such as from the opposite hand's finger. This extra contact disrupts continuous BP monitoring during daily activities. A proposed neckband-based device allows continuous BP measurement just by wearing it, and a proposed baseline-tracking impedance plethysmogram (IPG) facilitates daily-life BP measurement based on the PTT at the carotid artery [1]. For this purpose, a mixed-mode baseline cancellation (MM-BC) scheme is proposed to achieve wide-range fine-canceling capability for baseline wanders; this improves upon conventional bio-impedance methods that adopt only one analog-tuned [2] or digital-assisted [3] control loop. For fine and robust detection of the IPG, a two-step CT ADC is designed to include an artifact detection scheme. For effective support of 4-electrode (4E) and 2-electrode (2E) setups in bio-impedance measurements, a proposed phase-synchronization scheme requires only one channel for the I/Q detection, while conventional schemes utilize two channels [2]–[5]. For system-level verification, a bio-potential channel for the ECG is also integrated, and the IPG-based BP function is experimentally verified through a neckband device prototype.

B. Singh, Shakti Singh, Shikhar Verma et al.

Considering the food and nutritional security concerns, and post green revolution second generation problems i.e. increasing input use with declining efficiency trends, deteriorating soil health, depleting water resources, pollution, and narrowing profits at the end of farmers, an investigation was carried out on Wheat (Triticum aestivum) crop during 2019-20 at the crop research centre of SVPUA & T, Meerut (U.P.) to overcome these problems. Novel nutrient sources and their modes of applications with 14 treatments consisting of control, basal applications of recommended 100% NPK (150:60:40), 75% NPK (112.5:45:30) + water spray + nano N (4 ml l-1) + bio nano P (40 ml l-1) + bio nano K (40 ml l-1) + bio nano Zn (10 ml l-1) in various combinations were attempted on wheat variety DBW17 in randomized complete block design (RCBD) with three replications. The results of the study revealed that wheat grown with 75% NPK + nano nutrients (N + P + K + Zn) attainted significantly better growth as reflected by taller plants (91.7 cm), more no. of tillers m-1 row length (61.8), and higher dry matter accumulation m-1 row length (239.2), recorded at harvest with grain yield of (52.4 q ha-1). The crop contained 53.2 ppm Zn in grain and 31.8 ppm Zn in straw. Applications of nano nutrients – N, P, K, Zn and N +P + K + Zn +75% NPK worked synergistically and increased grain yields by 17.9, 15.7 14.5, 16.5 and 26.9% over 100% NPK. Thus, the wheat crop grown with the application of Nano-N + 75% NPK followed Nano-Zn + 75% NPK by had attained better growth (plant height, no. of the tiller, dry matter accumulation, yield (grain), nutrient content, and nutrient uptake.

Yan Su, Lin Tao, Xiaoli Zhang et al.

Dalitong Granules, a potent gastrointestinal motility promoting traditional Chinese medicine, is used to treat functional dyspepsia clinically. It shows good effect on alleviating gastrointestinal motility disorders and has a broad prospect of clinical application. However, there is no comprehensive study on its in vivo and in vitro chemical analysis. UPLC-Q-TOF-MS combined with the non-targeted characteristic filter analysis and in silico prediction strategies (NCFS) were used to deduce and identify the chemical components and in vivo metabolites in the bio-samples of rats following oral administration of Dalitong Granules. In this study, 108 chemical components were identified in Dalitong granules, including 50 flavonoids, 22 alkaloids, 13 terpenes, 11 organic acids, 10 coumarins and 2 volatile oils. In the plasma, tissue, urine and fecal samples of rats after administration of Dalitong granules, a total of 147 compounds were speculated (60 prototype compounds and 87 metabolites). The main metabolic pathways in vivo include methylation, demethylation, deglycosylation, hydrogenation, hydroxylation, sulfonation and glucuronidation as there are many flavonoids existing in Dalitong Granules. In conclusion, the chemical components and metabolites of Dalitong Granules were comprehensively identified by using a rapid and accurate analysis method, which laid a foundation for dissecting its bioactive substances. In addition, it provides a scientific basis for the in-depth study of the material basis of Dalitong Granules efficacy and its further comprehensive development and utilization.

Sijia Xiao, Xi-ke Xu, Xintong Wei et al.

Euphorbiasteroid, a lathyrane-type diterpene from Euphorbiae semen (the seeds of Euphorbia lathyris L.), has been shown to have a variety of pharmacological effects such as anti-tumor and anti-obesity. This study aims to investigate the metabolic profiles of euphorbiasteroid in rats and rat liver microsomes (RLMs) and Cunninghamella elegans bio-110930 by integrating ultra-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-Q/TOF-MS), UNIFI software, and NMR techniques. A total of 31 metabolites were identified in rats. Twelve metabolites (M1–M5, M8, M12–M13, M16, M24–M25, and M29) were matched to the metabolites obtained by RLMs incubation and the microbial transformation of C. elegans bio-110930 and their structures were exactly determined through analysis of NMR spectroscopic data. In addition, the metabolic pathways of euphorbiasteroid were then clarified, mainly including hydroxylation, hydrolysis, oxygenation, sulfonation, and glycosylation. Finally, three metabolites, M3 (20-hydroxyl euphorbiasteroid), M24 (epoxylathyrol) and M25 (15-deacetyl euphorbiasteroid), showed significant cytotoxicity against four human cell lines with IC50 values from 3.60 μM to 40.74 μM. This is the first systematic investigation into the in vivo metabolic pathways of euphorbiasteroid and the cytotoxicity of its metabolites, which will be beneficial for better predicting the metabolism profile of euphorbiasteroid in humans and understanding its possible toxic material basis.

Hadeel Elayan, Andrew W. Eckford, Raviraj Adve

It has been established that Terahertz (THz) band signals can interact with biomolecules through resonant modes. Specifically, of interest here, protein activation. Our research goal is to show how directing the mechanical signaling inside protein molecules using THz signals can control changes in their structure and activate associated biochemical and biomechanical events. To establish that, we formulate a selectivity metric that quantifies the system performance and captures the capability of the nanoantenna to induce a conformational change in the desired protein molecule/population. The metric provides a score between -1 and 1 that indicates the degree of control we have over the system to achieve targeted protein interactions. To develop the selectivity measure, we first use the Langevin stochastic equation driven by an external force to model the protein behavior. We then determine the probability of protein folding by computing the steady-state energy of the driven protein and then generalize our model to account for protein populations. Our numerical analysis results indicate that a maximum selectivity score is attained when only the targeted population experiences a folding behavior due to the impinging THz signal. From the achieved selectivity values, we conclude that the system response not only depends on the resonant frequency but also on the system controlling parameters namely, the nanoantenna force, the damping constant, and the abundance of each protein population. The presented work sheds light on the potential associated with the electromagnetic-based control of protein networks, which could lead to a plethora of applications in the medical field ranging from bio-sensing to targeted therapy.

Weijuan Xia, M. Khan, S. Khan et al.

Abstract Owing to the improved thermophysical properties of nano-materials, the nanofluid convey novel applications in industries, engineering as well as bio-medicals. With high efficiency performances, the nanoparticles include applications in various cooling and heating systems, energy production, aerospace engineering, thermal extrusion, bio-medical applications like treatment of diseases, brain tumour, surgical applications and many more. Moreover, the magneto-nanofluids have the characteristics in the flow of blood in the human artery. The current analysis deals with the free convective unsteady flow of viscous nanofluid subject to the magnetic force over a vertical plate in presence of porous space. The heat transfer phenomenon is accessed by utilizing the additional features like Joule heating, viscous dissipation and absorption coefficients. In addition to the volume fraction of the nanofluid, the effect of the chemical reaction is also introduced. The solution procedure for the modified time-dependent boundary value problem is performed by using the code in-build MATLAB namely Built-in-Shooting method. The change in velocity, temperature and concentration of nano-materials is examined through various graphs. The physical consequences of flow parameters like porosity parameter K p 1 ⩽ K p ⩽ 3 , thermal Grashoff number Gr 1 ⩽ G r ⩽ 3 , solutal Grashoff number Gc 1 ⩽ G c ⩽ 3 , magnetic field parameter M 1 ⩽ M ⩽ 3 , thermal radiation parameter Nr 0.1 ⩽ N r ⩽ 1 , radiation absorption parameter Q 0 ⩽ Q ⩽ 0.5 , Prandtl number Pr 2 ⩽ Pr ⩽ 21 , Eckert number Ec 0 ⩽ E c ⩽ 0.4 , heat source S - 0.2 ⩽ S ⩽ 0.2 , chemical reaction parameter Kc 1 ⩽ K c ⩽ 4 , nanoparticles volume fraction ϕ 0.0 ⩽ ϕ ⩽ 0.1 , Lewis number Le 1 ⩽ L e ⩽ 10 on velocity, temperature and concentration distributions is graphically analyzed. The results show that skin friction coefficients decline with buoyant forces. A lower numerical variation in Nusselt number is resulted with heat source parameter. Moreover, a sharp fall in nanofluid concentration is noticed with increment of chemical reactants and Lewis number.

Thomas Grentzinger, Stefan Oberlin, Grégory Schott et al.

Abstract Diverse classes of silencing small (s)RNAs operate via ARGONAUTE-family proteins within RNA-induced-silencing-complexes (RISCs). Here, we have streamlined various embodiments of a Q-sepharose-based RISC-purification method that relies on conserved biochemical properties of all ARGONAUTEs. We show, in multiple benchmarking assays, that the resulting 15-min benchtop extraction procedure allows simultaneous purification of all known classes of RISC-associated sRNAs without prior knowledge of the samples-intrinsic ARGONAUTE repertoires. Optimized under a user-friendly format, the method – coined ‘TraPR’ for Trans-kingdom, rapid, affordable Purification of RISCs – operates irrespectively of the organism, tissue, cell type or bio-fluid of interest, and scales to minute amounts of input material. The method is highly suited for direct profiling of silencing sRNAs, with TraPR-generated sequencing libraries outperforming those obtained via gold-standard procedures that require immunoprecipitations and/or lengthy polyacrylamide gel-selection. TraPR considerably improves the quality and consistency of silencing sRNA sample preparation including from notoriously difficult-to-handle tissues/bio-fluids such as starchy storage roots or mammalian plasma, and regardless of RNA contaminants or RNA degradation status of samples.

C. Bailly, Jin-Ming Gao

The presence of a fused 5/6/7 tricyclic core characterizes the group of cyathane diterpene natural products, that include more than 170 compounds, isolated from fungi such as Cyathus africanus and Hericium erinaceus. These compounds have a common biosynthetic precursor (cyatha-3,12-diene) and can be produced bio- or hemi-synthetically, or via total syntheses. Cyathane diterpenes display a range of pharmacological properties, including anti-inflammatory (possibly through binding to the iNOS protein) and neuroprotective effects. Many cyathanes like cyahookerin C, cyathin Q and cyafranines B and G can stimulate neurite outgrowth in cells, whereas conversely a few molecules (such as scabronine M) inhibit NGF-stimulated neurite outgrowth. The main anticancer cyathanes are erinacine A and cyathins Q and R, with a capacity to trigger cancer cell death dependent on the production of reactive oxygen species (ROS). These compounds, active both in vitro and in vivo, activate different signaling pathways in tumor cells to induce apoptosis (and autophagy) and to upregulate the expression of several proteins implicated in the organization and functioning of the actin cytoskeleton. An analysis of the functional analogy between erinacine A and other natural products known to interfere with the actin network in a ROS-dependent manner (notably cucurbitacin B) further supports the idea that erinacine A functions as a perturbator of the cytoskeleton organization. Collectively, we provide an overview of the molecular diversity of cyathane diterpenes and the main mechanisms of action of the lead compounds, with the objective to encourage further research with these fungal products. The anticancer potential of erinacine A deserves further attention but it will be necessary to better characterize the implicated targets and signaling pathways.

A. Kadam, B. Sharma, G. Saratale et al.

Nidhi Pandit, R. Jaiswal, N. Pathak

This paper reports a label-free, highly sensitive plasmonic metamaterial inspired multi-band planar microwave sensor for aqueous biological samples. The proposed sensor consists of a spoof surface whispering gallery mode (SS-WGM) resonator connected to a spoof surface plasmons polariton (SSPP) transmission line in a special arrangement. The SS-WGM resonator of the proposed sensor is capable of localizing the electromagnetic (EM) field into a specific region due to its slow-wave propagation characteristics. This feature enhances the interaction time of the sample under test (SUT) with EM wave and offers higher sensitivity. The EM wave localization enables the proposed design to sense the small volume of the bio-samples. This is required in the case of the aqueous samples because a large volume of SUT absorbs radio frequency (RF) signals and reduces the ${Q}$ -factor of the sensors. The microfluidic approach has been adopted as it supports a small sample size. The proposed sensor is numerically analyzed and optimized for multi-band capability using full-wave EM simulation. To ensure maximum sensitivity, the microfluidic channel is kept appropriately above the hot-spot of the sensor. Glucose aqueous solution is being used here as a biological sample. Experimental validation of the proposed approach has been done using different concentrations of the SUT. The proposed sensor offers a maximum measured sensitivity of 77.3e−02 MHz/mgml−1, which shows a fair improvement in the sensitivity as compared to the state-of-the-art. It is anticipated that the proposed microfluidic planar microwave sensor is paving the path for the development of the microwave-based modern lab-on-chip system arrangement.

A. Yadav, Ashutosh Kumar Singh, Jatin Phogat et al.

ETHNOPHARMACOLOGICAL RELEVANCE Tinospora cordifolia (TC) is being used as a blood purifier in Ayurveda since ancient time. It is a very popular immunomodulator and holds anti-inflammatory and anti-oxidative potential, hence anti-aging properties. Therefore, it is also known as 'Amrita' in Ayurveda and is widely used to treat diabetes mellitus type II (T2DM) and its secondary complications; however, its underlying mechanism was not expedited to date. AIM-: To explore the in vivo therapeutic efficiency and mechanism of action of TC and its secondary constitute magnoflorine on the skeletal muscle atrophy in the rat model of T2DM. METHOD Animal model of T2DM was developed using streptozotocin (STZ) injection followed by intervention with TC, metformin, and magnoflorine for three weeks. Confirmation of T2DM and abrogation of atrophic markers and possible mechanisms on supplementation of TC and magnoflorine were explored using histology, bio-assays, Western blotting, and q-PCR. RESULT TC and Magnoflorine supplementations significantly (p ≤ 0.05) decreased the fasting blood glucose (FBG) levels in T2DM rats. Both treatments prevented the lean body, individual skeletal muscle mass, and myotubes diameter loss (p ≤ 0.05). Magnoflorine significantly reduced the degradation of the protein indicated by biochemical markers of atrophy i.e. decreased serum creatine kinase (CK) levels and increased myosin heavy chain-β (MyHC-β) levels in muscles. Q-PCR and western blotting supported the findings that magnoflorine significantly increased the mRNA and protein abundances (~3 fold) of MyHC-β.TC and magnoflorine efficiently decreased the expression of ubiquitin-proteasomal E3-ligases (Fn-14/TWEAK, MuRF1, and Atrogin 1), autophagy (Bcl-2/LC3B), and caspase related genes along with calpains activities in T2DM rats. Both TC and magnoflorine also increased the activity of superoxide dismutase, GSH-Px, decreased the activities of β-glucuronidase, LPO, and prevented any alteration in the catalase activity. In contrast, magnoflorine increased expression of TNF-α and IL-6 whereas TC and metformin efficiently decreased the levels of these pro-inflammatory cytokines (p ≤ 0.05). However, magnoflorine was found to increase phosphorylation of Akt more efficiently than TC and metformin. CONCLUSION TC, and magnoflorine are found to be effective to control fasting blood glucose levels significantly in T2DM rats. It also promoted the Akt phosphorylation, suppressed autophagy and proteolysis that might be related to blood glucose-lowering efficacy of magnoflorine and TC. However, increased muscle weight, specifically of the soleus muscle, expression of IL-6, and slow MyHC indicated the increased myogenesis in response to magnoflorine and independent from its hypoglycemic activity.

Yi Zhou, Zhihe Guo, Wenjie Zhou et al.

We present a systematic investigation on the enhancement of the quality (Q) factors for guided-mode resonance (GMR) sensors with shallow subwavelength grating structures. By introducing the coupled-mode model, a theoretical high-Q factor can be achieved by choosing the proper geometric structure. Based on this method, a GMR sensor with a Q factor up to 8000, which is an order of magnitude larger than those of typical GMR sensors with Q factors within 100 ∼ 300, was demonstrated experimentally. Besides, the approached GMR sensor achieved a bulk sensitivity of 135 nm RIU−1 with a high signal to noise ratio, which supports a detection limit of 1 ng ml−1 for bovine serum albumin detection. This high performance GMR sensor paves the way towards high-throughput industrial mass production, and shows great potential for other applications, such as optical filters, spectrometer, and bio-imaging.

Judit Aizpuru, Annina Karolin Kemmer, Jong Woo Kim et al.

High throughput experimental systems play an important role in bioprocess development, as they provide an efficient way of analysing different experimental conditions and perform strain discrimination in previous phases to the industrial scale production. In the millilitre scale, these systems are combinations of parallel mini-bioreactors, liquid handling robots and automated workflows for data handling and model based operation. For successfully monitoring cultivation conditions and improving the overall process quality by model-based approaches, a proper model identification is crucial. However, the quality and amount of measurements makes this task challenging considering the complexity of the bio-processes. TheDissolved Oxygen Tension is often the only measurement which is available online, and therefore, a good understanding of the errors in this signal is important for performing a robust estimation.Some of the expected errors will provoke uncertainties in the time-domain of the measurement, and in those cases, the common Weighted Least Squares estimation procedure can fail providing good results. Moreover, these errors will have even a larger effect in the fed-batch phase where bolus feeding is applied, as this generates fast dynamic responses in the signal. In the present work, an insilico study of the performance of Weighted Least Squares estimator is analysed when the expected time-uncertainties are present in the oxygen signal. As an alternative, a loss based on the Dynamic Time Warping measure is proposed. The results show how this latter procedure outperforms the former reconstructing the oxygen signal, and in addition, returns less biased parameter estimates.

Peter Pfaffelhuber, Angelika Rohde

The concept of individual admixture (IA) assumes that the genome of individuals is composed of alleles inherited from $K$ ancestral populations. Each copy of each allele has the same chance $q_k$ to originate from population $k$, and together with the allele frequencies $p$ in all populations at all $M$ markers, comprises the admixture model. Here, we assume a supervised scheme, i.e.\ allele frequencies $p$ are given through a reference database of size $N$, and $q$ is estimated via maximum likelihood for a single sample. We study laws of large numbers and central limit theorems describing effects of finiteness of both, $M$ and $N$, on the estimate of $q$. We recall results for the effect of finite $M$, and provide a central limit theorem for the effect of finite $N$, introduce a new way to express the uncertainty in estimates in standard barplots, give simulation results, and discuss applications in forensic genetics.

Y. Wang, Liping Bi, Heng Zhang et al.

Abstract Equilibrium swelling test was employed to determine the swelling responses of Fluororubber (FKM) and Fluorosilicone Rubber (FVMQ) in different types of solvents, and the one-dimensional solubility parameters of FKM and FVMQ were obtained with a range of 17–25 MPa1/2 and 16–20 MPa1/2 respectively. It was attempted to calculate the three-dimensional solubility parameters (HSP) of FKM and FVMQ by using a professional computer software, and a new type of bio-fuel, represented by biodiesel, was further selected as an object in this work to investigate its swellability. Flory-Huggins interaction parameter (χHSP) between FKM, FVMQ and each solvents used in this work was calculated by HSP values, and the results turned out that the swelling ratio (q) decreased with increasing χHSP value. By mathematical fitting, two functional relationships between q and χHSP for FKM and FVMQ were achieved to predict the swelling properties of rubber in fluids. The increasing use of biodiesel with their effect on rubber materials has become an important topic of industrial research. The second components, including ethanol and ethylene glycol, were added into biodiesel with the purpose of cost reduction, which however might cause a critical increase of rubber swelling. One of the possible applications of χHSP values is the prediction or explanation of swelling behavior. Therein, χHSP values between FKM, FVMQ and biodiesel/additive blends were calculated and found that χHSP firstly decreased and then increased with increase of the additive (ethanol and ethylene glycol) content. Taking advantage of this tendency one can predict the swelling properties of rubber products in the biodiesel blends and provide an important reference for such applications in liquid medium.