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In this study, starch was modified with different amounts of boric acid, and the degree of substitution of the synthesized compounds at different temperatures and times was determined by Fourier transform infrared (FTIR) and titration methods. The degrees of substitution in both methods showed similar tendency. Optimum conditions were determined, and thermoplastic starch (TPS) and boric acid (B) films were prepared by casting method using glycerol as plasticizer. The morphological properties of the films were studied by scanning electron microscope-energy dispersive spectrometry (SEM-EDS). According to the FTIR results, it was observed that the B--O and B--O--C stretching vibrations increased when the amount of boric acid increased. The tensile strength, water barrier (swelling capacity, solubility, and water vapor permeability), and optical properties of samples were investigated. The swelling capacity, solubility, and water vapor permeability of the films decreased with the addition of 0.5% boric acid to the starch, but the highest tensile strength was achieved. The tensile strength for TPSB0.5% increased compared to control group TPS. In addition, the film showed the highest transparency. Thermoplastic film containing 0.5% boric acid can be used as a packaging material due to its improved mechanical and water barrier. Highlights * The thermoplastic starch and boric acid composite were successfully produced. * The presence of boric acid was confirmed by energy dispersive spectrometry (EDS). * The degree of substitution was determined by two different methods. * Transparency increased with the addition of boric acid. * Tensile strength improved with boric acid content. KEYWORDS barrier, boric acid, degree of substitution, tensile strength, thermoplastic starch, 1 | INTRODUCTION With the population in the world increases at an alarming rate, there will be an increase in demand for food supply and the subsequent regular use of [...]

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a liver pathology. NAFLD’s prevalence is increasing in the world and because of the increase non-invasive methods are gaining importance in diagnosis. The Fibrosis 4 (FIB-4) score is one of the most commonly used non-invasive scoring methods for diagnosing NAFLD today. Acute pancreatitis is a disease with inflammation and high morbidity. There are studies indicating that acute pancreatitis progresses more severely in patients with NAFLD. In our study, it was aimed to define the possible relationship between the FIB-4 score and the severity of acute pancreatitis, which has not been investigated before and contribute to the literature. Methods Our study was conducted by retrospectively scanning 124 patients diagnosed with acute pancreatitis between 2018 and 2020. The patients were compared with the presence of NAFLD and the FIB-4 score results in those with NAFLD. Results The 48th-hour Ranson score was found to be statistically significantly higher in patients with NAFLD compared to those without NAFLD. In patients with NAFLD, the total billurbin, direct billurbin, AST, ALT, ALP, GGT, and Ranson scores were found to be statistically significantly higher in those with FIB-4 score ≥ 2.67. Conclusıon As liver fibrosis increases in patients with NAFLD, it is suggested that liver damage accompanying acute pancreatitis increases, and the prognosis of acute pancreatitis worsens. There is a need for further studies to be conducted while centering more on this subject, which has not been investigated before in the literature.

Since the authorization of the Moderna mRNA COVID-19 vaccine, real-world evidence has indicated its effectiveness in preventing COVID-19 cases. However, increased cases of mRNA vaccine-associated myocarditis/pericarditis have been reported, predominantly in young adults and adolescents. The Food and Drug Administration conducted a benefit-risk assessment to inform the review of the Biologics License Application for use of the Moderna vaccine among individuals ages 18 and older. We modeled the benefit-risk per million individuals who receive two complete doses of the vaccine. Benefit endpoints were vaccine-preventable COVID-19 cases, hospitalizations, intensive care unit (ICU) admissions, and deaths. The risk endpoints were vaccine-related myocarditis/pericarditis cases, hospitalizations, ICU admissions, and deaths. The analysis was conducted on the age-stratified male population due to data signals and previous work showing males to be the main risk group. We constructed six scenarios to evaluate the impact of uncertainty associated with pandemic dynamics, vaccine effectiveness (VE) against novel variants, and rates of vaccine-associated myocarditis/pericarditis cases on the model results. For our most likely scenario, we assumed the US COVID-19 incidence was for the week of December 25, 2021, with a VE of 30% against cases and 72% against hospitalization with the Omicron-dominant strain. Our source for estimating vaccine-attributable myocarditis/pericarditis rates was FDA’s CBER Biologics Effectiveness and Safety (BEST) System databases. Overall, our results supported the conclusion that the benefits of the vaccine outweigh its risks. Remarkably, we predicted vaccinating one million 18–25 year-old males would prevent 82,484 cases, 4,766 hospitalizations, 1,144 ICU admissions, and 51 deaths due to COVID-19, comparing to 128 vaccine-attributable myocarditis/pericarditis cases, 110 hospitalizations, zero ICU admissions, and zero deaths. Uncertainties in the pandemic trajectory, effectiveness of vaccine against novel variants, and vaccine-attributable myocarditis/pericarditis rate are important limitations of our analysis. Also, the model does not evaluate potential long-term adverse effects due to either COVID-19 or vaccine-attributable myocarditis/pericarditis.

A key unmet need in the management of hemophilia A (HA) is the lack of clinically validated markers that are associated with the development of neutralizing antibodies to Factor VIII (FVIII) (commonly referred to as inhibitors). This study aimed to identify relevant biomarkers for FVIII inhibition using Machine Learning (ML) and Explainable AI (XAI) using the My Life Our Future (MLOF) research repository. The dataset includes biologically relevant variables such as age, race, sex, ethnicity, and the variants in the F8 gene. In addition, we previously carried out Human Leukocyte Antigen Class II (HLA-II) typing on samples obtained from the MLOF repository. Using this information, we derived other patient-specific biologically and genetically important variables. These included identifying the number of foreign FVIII derived peptides, based on the alignment of the endogenous FVIII and infused drug sequences, and the foreign-peptide HLA-II molecule binding affinity calculated using NetMHCIIpan. The data were processed and trained with multiple ML classification models to identify the top performing models. The top performing model was then chosen to apply XAI via SHAP, (SHapley Additive exPlanations) to identify the variables critical for the prediction of FVIII inhibitor development in a hemophilia A patient. Using XAI we provide a robust and ranked identification of variables that could be predictive for developing inhibitors to FVIII drugs in hemophilia A patients. These variables could be validated as biomarkers and used in making clinical decisions and during drug development. The top five variables for predicting inhibitor development based on SHAP values are: (i) the baseline activity of the FVIII protein, (ii) mean affinity of all foreign peptides for HLA DRB 3, 4, & 5 alleles, (iii) mean affinity of all foreign peptides for HLA DRB1 alleles), (iv) the minimum affinity among all foreign peptides for HLA DRB1 alleles, and (v) F8 mutation type.

Cancer vaccines are an important component of the cancer immunotherapy toolkit enhancing immune response to malignant cells by activating CD4+ and CD8+ T cells. Multiple successful clinical applications of cancer vaccines have shown good safety and efficacy. Despite the notable progress, significant challenges remain in obtaining consistent immune responses across heterogeneous patient populations, as well as various cancers. We present a mechanistic mathematical model describing key interactions of a personalized neoantigen cancer vaccine with an individual patient's immune system. Specifically, the model considers the vaccine concentration of tumor-specific antigen peptides and adjuvant, the patient's major histocompatibility complexes I and II copy numbers, tumor size, T cells, and antigen presenting cells. We parametrized the model using patient-specific data from a clinical study in which individualized cancer vaccines were used to treat six melanoma patients. Model simulations predicted both immune responses, represented by T cell counts, to the vaccine as well as clinical outcome (determined as change of tumor size). This model, although complex, can be used to describe, simulate, and predict the behavior of the human immune system to a personalized cancer vaccine.

Drug resistance is a significant obstacle to effective cancer treatment. To gain insights into how drug resistance develops, we adopted a concept called fitness landscape and employed a phenotype-structured population model by fitting to a set of experimental data on a drug used for ovarian cancer, olaparib. Our modeling approach allowed us to understand how a drug affects the fitness landscape and track the evolution of a population of cancer cells structured with a spectrum of drug resistance. We also incorporated pharmacokinetic (PK) modeling to identify the optimal dosages of the drug that could lead to long-term tumor reduction. We derived a formula that indicates that maximizing variation in plasma drug concentration over a dosing interval could be important in reducing drug resistance. Our findings suggest that it may be possible to achieve better treatment outcomes with a drug dose lower than the levels recommended by the drug label. Acknowledging the current limitations of our work, we believe that our approach, which combines modeling of both PK and drug resistance evolution, could contribute to a new direction for better designing drug treatment regimens to improve cancer treatment. [ABSTRACT FROM AUTHOR]

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patient's cancer. These vaccines can provide significant clinical benefit by leveraging the patient's immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulate a mathematical dose optimization problem based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. We propose an optimization approach to identify the optimal personalized vaccine doses, considering a fixed vaccination schedule, while simultaneously minimizing the overall number of tumor and activated T cells. To validate our approach, we perform in silico experiments on six real-world clinical trial patients with advanced melanoma. We compare the results of applying an optimal vaccine dose to those of a suboptimal dose (the dose used in the clinical trial and its deviations). Our simulations reveal that an optimal vaccine regimen of higher initial doses and lower final doses may lead to a reduction in tumor size for certain patients. Our mathematical dose optimization offers a promising approach to determining an optimal vaccine dose for each patient and improving clinical outcomes. Author summary: The development of neoantigen cancer vaccines have rapidly increased over the past decade with the advancement of next-generation sequencing technologies to determine immunogenic peptides from patient's somatic mutations. However, traditional methods to determine the cancer vaccine dose often produce suboptimal clinical outcomes. This work use our previous mathematical model to represent the immunological cascade at the cellular and subcellular levels elicited by the vaccine dose, and focuses on developing a mathematical optimization approach to identify the optimal vaccine dose to minimize two objective functionals, (i) minimize the amount of peptide dose and tumor size, and (ii) minimize the number of activated T cells in addition to the objective functional i. The optimization approach allows the identification of optimal vaccine doses among a set of tested doses for a higher clinical benefit in tumor reduction. To demonstrate and validate our optimization approach, we perform in silico experiments on six patients with melanoma from a clinical trial study. The results show that the predicted optimal vaccine doses can provide higher clinical benefit in tumor reduction when compared to the clinical trial doses for some patients. [ABSTRACT FROM AUTHOR]

Immune responses to therapeutic proteins and peptides can adversely affect their safety and efficacy; consequently, immunogenicity risk-assessments are part of the development, licensure and clinical use of these products. In most cases the development of anti-drug antibodies is mediated by T cells which requires antigen presentation by Major Histocompatibility Complex Class II (MHCII) molecules (also called Human Leucocyte Antigen, HLA in humans). Immune responses to many protein therapeutics are thus HLA-restricted and it is important that the distribution of HLA variants used in the immunogenicity assessments provides adequate coverage of the target population. Due to biases inherent to the collection of samples in a blood bank or donor pool, simple random sampling will not achieve a truly representative sample of the population of interest. To help select a donor cohort we introduce SampPick, an implementation of simulated annealing which optimizes cohort selection to closely match the frequency distribution of a target population or subpopulation. With inputs of a target background frequency distribution for a population and a set of available, HLA-typed donors, the algorithm will iteratively create a cohort of donors of a user selected size that will closely match the target population rather than a random sample. In addition to optimizing the HLA types of donor cohorts, the software presented can be used to optimize donor cohorts for any other biallelic or monoallelic trait.

Cell division, endocytosis, and viral budding would not function without the localization and assembly of protein complexes on membranes. What is poorly appreciated, however, is that by localizing to membranes, proteins search in a reduced space that effectively drives up concentration. Here we derive an accurate and practical analytical theory to quantify the significance of this dimensionality reduction in regulating protein assembly on membranes. We define a simple metric, an effective equilibrium constant, that allows for quantitative comparison of protein-protein interactions with and without membrane present. To test the importance of membrane localization for driving protein assembly, we collected the protein-protein and protein-lipid affinities, protein and lipid concentrations, and volume-to-surface-area ratios for 46 interactions between 37 membrane-targeting proteins in human and yeast cells. We find that many of the protein-protein interactions between pairs of proteins involved in clathrin-mediated endocytosis in human and yeast cells can experience enormous increases in effective protein-protein affinity (10-1000 fold) due to membrane localization. Localization of binding partners thus triggers robust protein complexation, suggesting that it can play an important role in controlling the timing of endocytic protein coat formation. Our analysis shows that several other proteins involved in membrane remodeling at various organelles have similar potential to exploit localization. The theory highlights the master role of phosphoinositide lipid concentration, the volume-to-surface-area ratio, and the ratio of 3D to 2D equilibrium constants in triggering (or preventing) constitutive assembly on membranes. Our simple model provides a novel quantitative framework for interpreting or designing in vitro experiments of protein complexation influenced by membrane binding.

Localization of proteins to a membrane is an essential step in a broad range of biological processes such as signaling, virion formation, and clathrin-mediated endocytosis. The strength and specificity of proteins binding to a membrane depend on the lipid composition. Single-particle reaction-diffusion methods offer a powerful tool for capturing lipid-specific binding to membrane surfaces by treating lipids explicitly as individual diffusible binding sites. However, modeling lipid particle populations is expensive. Here, we present an algorithm for reversible binding of proteins to continuum surfaces with implicit lipids, providing dramatic speed-ups to many body simulations. Our algorithm can be readily integrated into most reaction-diffusion software packages. We characterize changes to kinetics that emerge from explicit vs implicit lipids as well as surface adsorption models, showing excellent agreement between our method and the full explicit lipid model. Compared to models of surface adsorption, which couple together binding affinity and lipid concentration, our implicit lipid model decouples them to provide more flexibility for controlling surface binding properties and lipid inhomogeneity, thus reproducing binding kinetics and equilibria. Crucially, we demonstrate our method's application to membranes of arbitrary curvature and topology, modeled via a subdivision limit surface, again showing excellent agreement with explicit lipid simulations. Unlike adsorption models, our method retains the ability to bind lipids after proteins are localized to the surface (through, e.g., a protein-protein interaction), which can greatly increase the stability of multiprotein complexes on the surface. Our method will enable efficient cell-scale simulations involving proteins localizing to realistic membrane models, which is a critical step for predictive modeling and quantification of in vitro and in vivo dynamics. [ABSTRACT FROM AUTHOR]

The US FDA Center for Biologics Evaluation and Research (CBER) is responsible for the regulation of biologically derived products. FDA has established Advisory Committees (AC) as vehicles to seek external expert advice on scientific and technical matters related to the development and evaluation of products regulated by the agency. We aimed to identify and evaluate common topics discussed in CBER AC meetings during the regulatory decision-making process for biological products and medical devices. We analyzed the content of 119 CBER-led AC meetings between 2009 and 2021 listed on the FDA AC webpage. We reviewed publicly available meeting materials such as briefing documents, summaries, and transcripts. Using a structured review codebook based on FDA benefit-risk guidance, we identified important considerations within the benefit-risk dimensions discussed at the AC meetings: therapeutic context, benefit, risk and risk management, and benefit-risk trade-off, where evidence and uncertainty are critical parts of the FDA benefit-risk framework. Based on a detailed review of 24 topics discussed in 23 selected AC meetings conducted between 2016 and 2021, the two most frequently discussed considerations were “Uncertainty about assessment of the safety profile” and “Uncertainty about assessment of the benefit based on clinical trial data” (16/24 times each) as defined in our codebook. Most of the reviewed meetings discussed Investigational New Drug or Biologics License Applications of products. This review could help sponsors better plan and design studies by contextualizing how the benefit-risk dimensions were embedded in the AC discussions and the considerations that went into the final AC recommendations. [ABSTRACT FROM AUTHOR]

• Benefits/risks of Comirnaty vaccine evaluated, groups under 30, to inform decisions. • Disease prevention from vaccine compared to risks, such as myocarditis/pericarditis. • Model demonstrates benefits of Comirnaty clearly outweigh risks for analyzed groups. • Higher COVID-19 incidence associated with greater vaccine benefits and vice versa. Since authorization of the Pfizer-BioNTech COVID-19 Vaccine, mRNA (Comirnaty), real-world evidence has indicated the vaccines are effective in preventing COVID-19 cases and related hospitalizations and deaths. However, increased cases of myocarditis/pericarditis have been reported in the United States associated with vaccination, particularly in adolescents and young adults. FDA conducted a benefit-risk assessment to determine whether the benefits of vaccination outweigh the risks among various age (16–17, 18–24, 25–29) and sex (M/F) subgroups being considered for approved use of the vaccine. We conducted a simulation study with sensitivity analysis of the benefits and risks of the vaccine across possible pandemic scenarios. The model results show benefits outweigh the risks for all scenarios including the high-risk subgroup, males 16–17 years old. Our worst-case scenario used sex and age subgroup-specific incidences for COVID-19 cases (47–98 per million per day) and hospitalizations (1–4 per million per day) which are the US COVID-19 incidences as of July 10, 2021, vaccine efficacy of 70% against COVID-19 cases and 80% against hospitalization, and unlikely, pessimistic, non-zero vaccine-attributable myocarditis death rate. For males 16–17 years old, the model predicts prevented COVID cases, hospitalizations, ICUs, and deaths of 13577, 127, 41, and 1, respectively; while the predicted ranges for excess myocarditis/pericarditis cases, hospitalizations, and deaths attributable to the vaccine are [98–196], [98–196], and 0, respectively, for the worst-case scenario. Considering the different clinical implications of hospitalization due to COVID-19 infection versus vaccine-attributable myocarditis/pericarditis cases, we determine the benefits still outweigh the risks even for this high-risk subgroup. Our results demonstrate that the benefits of the vaccine outweigh its risks for all age and sex subgroups we analyze in this study. Uncertainties exist in this assessment as both benefits and risks of vaccination may change with the continuing evolution of the pandemic. [ABSTRACT FROM AUTHOR]

Research was conducted to examine the Urbach's tails of TlInS2 single crystals in the temperature range of 10-340 K. Five different convergent group of lines in alpha(E,T) with different E0 and alpha 0 convergence point coordinates were identified between 120-340 K while no convergence point of lines were evident below 120K. Differences in band gap jumping values were seen as caused by phase transitions and simultaneous influence of phonon coupling with electrons induced by exciton dissociation.

Research was conducted to study the temperature dependence of galvanomagnetic properties for Gd doped and undoped p-type GaSe. Magnetoresistance and Hall effect measurements were performed on undoped p-GaSe and Gd doped p-GaSe samples under the temperature range of 90-320 K and 60-320 K, respectively. Results indicate that the Gd atoms in p-GaSe are not electrically active but they both clean the impurities between the layers which act as acceptors and bring those, which are in the layers, into the inactive complexes.

The temperature- and magnetic field-dependence of the electron Hall mobility were measured in undoped n-type InSe and Er-doped InSe at temperatures of 10-340 K along with the transverse and longitudinal magnetoresistance effects. The results, which indicated that the erbium atoms are not electrically active, showed that the electrical conductivity of both samples lessens as the temperature increases for the temperature range 60-100 degrees Celsius.

Cancer vaccines are an important component of the cancer immunotherapy toolkit enhancing immune response to malignant cells by activating CD4+ and CD8+ T cells. Multiple successful clinical applications of cancer vaccines have shown good safety and efficacy. Despite the notable progress, significant challenges remain in obtaining consistent immune responses across heterogeneous patient populations, as well as various cancers. We present a mechanistic mathematical model describing key interactions of a personalized neoantigen cancer vaccine with an individual patient's immune system. Specifically, the model considers the vaccine concentration of tumor-specific antigen peptides and adjuvant, the patient's major histocompatibility complexes I and II copy numbers, tumor size, T cells, and antigen presenting cells. We parametrized the model using patient-specific data from a clinical study in which individualized cancer vaccines were used to treat six melanoma patients. Model simulations predicted both immune responses, represented by T cell counts, to the vaccine as well as clinical outcome (determined as change of tumor size). This model, although complex, can be used to describe, simulate, and predict the behavior of the human immune system to a personalized cancer vaccine. Author summary: Personalized cancer vaccines have gained attention in recent years due to the advances in sequencing techniques that have facilitated the identification of multiple tumor-specific mutations. This type of individualized immunotherapy has the potential to be specific, efficacious, and safe since it induces an immune response to protein targets not found on normal cells. This work focuses on understanding and analyzing important mechanisms involved in the activity of personalized cancer vaccines using a mechanistic mathematical model. This model describes the interactions of a personalized neoantigen peptide cancer vaccine, the human immune system and tumor cells operating at the molecular and cellular level. The molecular level captures the processing and presentation of neoantigens by dendritic cells to the T cells using cell surface proteins. The cellular level describes the differentiation of dendritic cells due to peptides and adjuvant concentrations in the vaccine, activation, and proliferation of T cells in response to treatment, and tumor growth. The model captures immune response behavior to a vaccine associated with patient-specific factors (e.g., different initial tumor burdens). This model enables the simulation of a complex biological system, the human immune system, by performing in silico experiments that may become the input for further analysis such as the identification of key parameters or mechanisms and/or interpretation of data. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the genetic diversity of 18 tea (Camellia sinensis) genotypes and 6 varieties collected from different provinces in the Black Sea region of Turkey. DNA isolation was performed according to the CTAB method of tea samples. PCR was performed for all DNA samples with 59 ISSR primers. In order to determine the genetic relationship between tea genotypes among these primers, 15 polymorphic ISSR primers were used. At the end of the PCR procedures, the polymorphism rate was determined as 77% from the bands formed. As a result of the analysis of products obtained by PCR studies, 109 bands were obtained in ISSR primers. The obtained bands were scored as presence (1) and absence (0) and their files were created. A genetic relation dendrogram was formed according to the obtained band results. Genetic diversity of tea genotypes and varieties of the Black Sea region was determined by molecular markers. [ABSTRACT FROM AUTHOR]

The dynamics of association between diffusing and reacting molecular species are routinely quantified using simple rate-equation kinetics that assume both well-mixed concentrations of species and a single rate constant for parameterizing the binding rate. In two-dimensions (2D), however, even when systems are well-mixed, the assumption of a single characteristic rate constant for describing association is not generally accurate, due to the properties of diffusional searching in dimensions d ≤ 2. Establishing rigorous bounds for discriminating between 2D reactive systems that will be accurately described by rate equations with a single rate constant, and those that will not, is critical for both modeling and experimentally parameterizing binding reactions restricted to surfaces such as cellular membranes. We show here that in regimes of intrinsic reaction rate (ka) and diffusion (D) parameters ka/D > 0.05, a single rate constant cannot be fit to the dynamics of concentrations of associating species independently of the initial conditions. Instead, a more sophisticated multiparametric description than rate-equations is necessary to robustly characterize bimolecular reactions from experiment. Our quantitative bounds derive from our new analysis of 2D rate-behavior predicted from Smoluchowski theory. Using a recently developed single particle reaction-diffusion algorithm we extend here to 2D, we are able to test and validate the predictions of Smoluchowski theory and several other theories of reversible reaction dynamics in 2D for the first time. Finally, our results also mean that simulations of reactive systems in 2D using rate equations must be undertaken with caution when reactions have ka/D > 0.05, regardless of the simulation volume. We introduce here a simple formula for an adaptive concentration dependent rate constant for these chemical kinetics simulations which improves on existing formulas to better capture non-equilibrium reaction dynamics from dilute to dense systems. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor (CAR) T-cell therapy is an immunotherapy that has recently become highly instrumental in the fight against life-threatening diseases. A variety of modeling and computational simulation efforts have addressed different aspects of CAR T-cell therapy, including T-cell activation, T- and malignant cell population dynamics, therapeutic cost-effectiveness strategies, and patient survival. In this article, we present a systematic review of those efforts, including mathematical, statistical, and stochastic models employing a wide range of algorithms, from differential equations to machine learning. To the best of our knowledge, this is the first review of all such models studying CAR T-cell therapy. In this review, we provide a detailed summary of the strengths, limitations, methodology, data used, and data gap in currently published models. This information may help in designing and building better models for enhanced prediction and assessment of the benefit-risk balance associated with novel CAR T-cell therapies, as well as with the data need for building such models. [ABSTRACT FROM AUTHOR]

Statistical thermodynamics of residue fluctuations of native proteins in a temperature, pressure, and force reservoir is formulated. The general theory is discussed in terms of harmonic and anharmonic fluctuations of residues. The two elastic network models based on the harmonic approximation, the anisotropic network and the Gaussian network models are discussed as the limiting cases of the general theory. The heat capacity and the correlations between the energy fluctuations and residue fluctuations are obtained for the harmonic approximation. The formulation is extended to large fluctuations of residues in order to account for effects of anharmonicity. The fluctuation probability function is constructed for this purpose as a tensorial Hermite series expansion with higher order moments of fluctuations as coefficients. Evaluation of the higher order moments using the proposed statistical thermodynamics model is explained. The formulation is applied to a hexapeptide and the fluctuations of residues obtained by molecular dynamics simulations are characterized in the framework of the model developed. In particular, coupling of two different modes in the nonlinear model is discussed in detail. [ABSTRACT FROM AUTHOR]

Background: Zika virus (ZIKV), a mosquito-borne flavivirus, causes asymptomatic infections in blood donors and can be transmitted by transfusion. During the 2016 US outbreak, universal individual-donation nucleic acid testing (ID-NAT) was used to screen the blood supply for ZIKV. Testing pooled samples from multiple donations with minipool (MP)-NAT is less sensitive than ID-NAT, which raised questions about its utility in ZIKV outbreaks.Study Design and Methods: A mathematical model and computer simulation determined the risk of missing ID-NAT-reactive and immunoglobulin (Ig) M-negative donations in a ZIKV outbreak if MP-NAT is used initially instead of ID-NAT. The model calculated the time required for ZIKV RNA to replicate to a concentration detectable by testing donations individually or in pools of 6 (MP6) or 16 (MP16). A computer simulation then randomly selected infection times to determine the probability of detection by the candidate tests.Results: The probability of detecting the first ID-NAT-reactive unit in an outbreak is 92% (2.5th-97.5th percentile, 79%-99%) by MP6 and 85% (2.5th-97.5th percentile, 67%-99%) by MP16. When one donation is detected by MP-NAT, the model predicts that the chance of having missed one or more ID-NAT-reactive donations is 8% to 15%. The probability of missing a unit by MP-NAT is constant over the course of the outbreak (8% by MP6, 15% by MP16).Conclusion: The model predicts that the probability that a candidate MP-NAT will detect the first ID-NAT-reactive unit in a ZIKV outbreak is 85% to 92% and remains constant over time. [ABSTRACT FROM AUTHOR]

In this study, the rheological behaviour of semi-solid peanut butter samples with a fat ratio of 42.7% were examined in a temperature interval of 10-50 °C via rotational rheometer at different shear rates. Peanut butter samples display non-Newtonian yield- pseudoplastic behaviour. Seven model equalities were used for representing the time-independent rheological behaviour of peanut butter and the representation ability of the models were determined by way of determination coefficient (r²), mean absolute per-centage error (MA%E), chi-square (χ²) and root mean square error (RMSE) statistical parameters. It was observed that the O'foli model was the most successful among the models applied to experimental data. Temperature dependency of viscosity at shear rates of 0.1, 0.4 and 0.75 s-1 was determined via Arrhenius equality and activation energies varied between the interval of 46818.63-51952.52 J/mol. Three-layer model suggested for estimating viscosity using artificial neural network method was observed to be quite successful. [ABSTRACT FROM AUTHOR]

Cell division, endocytosis, and viral budding would not function without the localization and assembly of protein complexes on membranes. What is poorly appreciated, however, is that by localizing to membranes, proteins search in a reduced space that effectively drives up concentration. Here we derive an accurate and practical analytical theory to quantify the significance of this dimensionality reduction in regulating protein assembly on membranes. We define a simple metric, an effective equilibrium constant, that allows for quantitative comparison of protein-protein interactions with and without membrane present. To test the importance of membrane localization for driving protein assembly, we collected the protein-protein and protein-lipid affinities, protein and lipid concentrations, and volume-to-surface-area ratios for 46 interactions between 37 membrane-targeting proteins in human and yeast cells. We find that many of the protein-protein interactions between pairs of proteins involved in clathrin-mediated endocytosis in human and yeast cells can experience enormous increases in effective protein-protein affinity (10–1000 fold) due to membrane localization. Localization of binding partners thus triggers robust protein complexation, suggesting that it can play an important role in controlling the timing of endocytic protein coat formation. Our analysis shows that several other proteins involved in membrane remodeling at various organelles have similar potential to exploit localization. The theory highlights the master role of phosphoinositide lipid concentration, the volume-to-surface-area ratio, and the ratio of 3D to 2D equilibrium constants in triggering (or preventing) constitutive assembly on membranes. Our simple model provides a novel quantitative framework for interpreting or designing in vitro experiments of protein complexation influenced by membrane binding. [ABSTRACT FROM AUTHOR]

The aim of this study was to detect the plasmid profiles, haemolytic activity and antibiotic susceptibility patterns of 47 Enterococcus strains isolated from Turkish Tulum cheese. Enterococcus strains were found to comprise 1-9 plasmids with molecular weight from 2.0 to 47.6 kb. None of the Enterococcus strains displayed haemolytic activity. The Enterococcus strains were found mostly resistant to tetracycline (30 μg) followed by high-level streptomycin (300 μg). The Enterococcus faecalis strains were found to be highly resistant to antibiotics than Enterococcus faecium and Enterococcus durans strains. In total, 4.3% of the strains exhibited multiple antibiotic resistance patterns. [ABSTRACT FROM AUTHOR]