Microbial signals drive pre-leukaemic myeloproliferation in a Tet2-deficient host.

Somatic mutations in tet methylcytosine dioxygenase 2 (TET2), which encodes an epigenetic modifier enzyme, drive the development of haematopoietic malignancies1-7. In both humans and mice, TET2 deficiency leads to increased self-renewal of haematopoietic stem cells with a net developmental bias towards the myeloid lineage1,4,8,9. However, pre-leukaemic myeloproliferation (PMP) occurs in only a fraction of Tet2-/- mice8,9 and humans with TET2 mutations1,3,5-7, suggesting that extrinsic non-cell-autonomous factors are required for disease onset. Here we show that bacterial translocation and increased interleukin-6 production, resulting from dysfunction of the small-intestinal barrier, are critical for the development of PMP in mice that lack Tet2 expression in haematopoietic cells. Furthermore, in symptom-free Tet2-/- mice, PMP can be induced by disrupting intestinal barrier integrity, or in response to systemic bacterial stimuli such as the toll-like receptor 2 agonist. PMP was reversed by antibiotic treatment and failed to develop in germ-free Tet2-/- mice, which illustrates the importance of microbial signals in the development of this condition. Our findings demonstrate the requirement for microbial-dependent inflammation in the development of PMP and provide a mechanistic basis for the variation in PMP penetrance observed in Tet2-/- mice. This study will prompt new lines of investigation that may profoundly affect the prevention and management of haematopoietic malignancies.

A comparison between the enzymatic oxidation method and headspace gas chromatography with a flame ionization detector in the determination of postmortem blood ethanol.

Ethanol is the most commonly encountered substance in forensic toxicology. Determining blood alcohol concentration (BAC) in autopsies accounts for the majority of work in forensic diagnosis. The most common method to assess BAC is the enzymatic oxidation method because of its low cost, easy operation, and high throughput. Still, the elevated lactate and lactate dehydrogenase (LDH) levels in postmortem blood may affect accuracy. This study uses headspace gas chromatography with a flame ionization detector (HS-GC/FID) to assess the interference of lactate and LDH levels on BAC in 110 autopsied blood samples determined by the enzymatic oxidation method. The results showed that lactate and LDH levels in postmortem blood were higher than in normal blood. There was a weak correlation between the lactate levels and BAC difference (r = 0.23, p < 0.05) and a strong correlation between LDH levels and BAC difference (r = 0.67, p < 0.001). The differentiation of BAC between the enzymatic oxidation method and HS-GC/FID was significant (p < 0.001), confirming the interference significantly. All postmortem blood samples with lactate and LDH levels higher than regular lead to a positive error in determining BAC by enzymatic oxidation method. The study results suggest that the HS-GC/FID method should be used to determine BAC in postmortem blood samples instead of the enzymatic oxidation method to avoid mistakes in forensic diagnosis.

When can we reconstruct the ancestral state? Beyond Brownian motion.

Reconstructing the ancestral state of a group of species helps answer many important questions in evolutionary biology. Therefore, it is crucial to understand when we can estimate the ancestral state accurately. Previous works provide a necessary and sufficient condition, called the big bang condition, for the existence of an accurate reconstruction method under discrete trait evolution models and the Brownian motion model. In this paper, we extend this result to a wide range of continuous trait evolution models. In particular, we consider a general setting where continuous traits evolve along the tree according to stochastic processes that satisfy some regularity conditions. We verify these conditions for popular continuous trait evolution models including Ornstein-Uhlenbeck, reflected Brownian Motion, bounded Brownian Motion, and Cox-Ingersoll-Ross.

When can we reconstruct the ancestral state? A unified theory.

Ancestral state reconstruction is one of the most important tasks in evolutionary biology. Conditions under which we can reliably reconstruct the ancestral state have been studied for both discrete and continuous traits. However, the connection between these results is unclear, and it seems that each model needs different conditions. In this work, we provide a unifying theory on the consistency of ancestral state reconstruction for various types of trait evolution models. Notably, we show that for a sequence of nested trees with bounded heights, the necessary and sufficient conditions for the existence of a consistent ancestral state reconstruction method under discrete models, the Brownian motion model, and the threshold model are equivalent. When tree heights are unbounded, we provide a simple counter-example to show that this equivalence is no longer valid.

Investigation of arsenic contamination in groundwater using hydride generation atomic absorption spectrometry.

The validated hydride generation atomic absorption spectrometry (HG-AAS) method has been used to investigate total arsenic in groundwater. Under optimal experimental conditions, the concentration of arsenic in groundwater can be analysed in the range of 0.5 to 50 µg/L, with a method detection limit of 0.15 µg/L. Its recovery in the field is from 96.3 to 99.8%, with high repeatability. The method was used to observe the total arsenic pollution in groundwater collected in Phu Tho Province, Vietnam. A total of 364 groundwater samples were analysed. The results showed that arsenic pollution was significant, with 15.93% of the samples higher than the maximum permissible level of arsenic. About 20.69% of the contaminated samples had a total arsenic ten times higher (100 µg/L) than the maximum permissible level of arsenic. The pollution source was also considered by comparing the arsenic level in the groundwater with arsenic in the surface water in the same areas. Thus, the use of the high-accuracy and sensitive method, HG-AAS, supplies valuable data on groundwater pollution for water resources management and environmental protection.

Prevalence, species distribution and antifungal susceptibility of Candida albicans causing vaginal discharge among symptomatic non-pregnant women of reproductive age at a tertiary care hospital, Vietnam.

BACKGROUND: Vaginal candidiasis is frequent in women of reproductive age. Accurate identification Candida provides helpful information for successful therapy and epidemiology study; however, there are very limited data from the Vietnam have been reported. This study was performed to determine the prevalence, species distribution of yeast causing vaginal discharge and antifungal susceptibility patterns of Candida albicans among symptomatic non-pregnant women of reproductive age. METHODS: Vaginal discharge samples were collected from 462 women of reproductive age in Hanoi, Vietnam between Sep 2019 and Oct 2020. Vaginal swabs from these patients were examined by direct microscopic examination (10% KOH). CHROMagar™ Candida medium and Sabouraud dextrose agar supplemented with chloramphenicol (0.5 g/l) were used to isolate yeast, and species identification was performed using morphological tests and molecular tools (PCR and sequencing). Antifungal susceptibility testing was determined according to the Clinical and Laboratory Standards Institute guidelines (M27-A3 and M27-S4). RESULTS: The prevalence of vaginal yeast colonization in non-pregnant women was 51.3% of 462 participants. Nine different yeast species were identified. Among these isolates, C. albicans (51.37%) was the most frequent, followed by C. parapsilosis (25.88%), C. glabrata (11.37%), C. tropicalis (4.31%), C. krusei (3.92%), C. africana (1.57%), Saccharomyces cerevisiae (0.78%), C. nivariensis (1 isolates, 0.39%), and C. lusitaniae (1 isolates, 0.39%), respectively. Among C. albicans, all 46 isolates were 100% susceptible to micafungin, caspofungin, and miconazole. The susceptibility rates to amphotericine B, 5-flucytosine, fluconazole, itraconazole and voriconazole were 95.65, 91.30, 91.30, 82.61 and 86.95%, respectively. CONCLUSIONS: The prevalence of VVC among symptomatic non-pregnant women of reproductive age in Vietnam was higher than many parts of the world. The high frequency of non-albicans Candida species, which were often more resistant to antifungal agents, was a notable feature. Resistance rates of vaginal C. albicans isolates to antifungal agents was low. Our findings suggest that continued surveillance of changes in species distribution and susceptibility to antifungals should be routinely screened and treated.

Joint Maximum Likelihood of Phylogeny and Ancestral States Is Not Consistent.

Maximum likelihood estimation in phylogenetics requires a means of handling unknown ancestral states. Classical maximum likelihood averages over these unknown intermediate states, leading to provably consistent estimation of the topology and continuous model parameters. Recently, a computationally efficient approach has been proposed to jointly maximize over these unknown states and phylogenetic parameters. Although this method of joint maximum likelihood estimation can obtain estimates more quickly, its properties as an estimator are not yet clear. In this article, we show that this method of jointly estimating phylogenetic parameters along with ancestral states is not consistent in general. We find a sizeable region of parameter space that generates data on a four-taxon tree for which this joint method estimates the internal branch length to be exactly zero, even in the limit of infinite-length sequences. More generally, we show that this joint method only estimates branch lengths correctly on a set of measure zero. We show empirically that branch length estimates are systematically biased downward, even for short branches.

On the convergence of the maximum likelihood estimator for the transition rate under a 2-state symmetric model.

Maximum likelihood estimators are used extensively to estimate unknown parameters of stochastic trait evolution models on phylogenetic trees. Although the MLE has been proven to converge to the true value in the independent-sample case, we cannot appeal to this result because trait values of different species are correlated due to shared evolutionary history. In this paper, we consider a 2-state symmetric model for a single binary trait and investigate the theoretical properties of the MLE for the transition rate in the large-tree limit. Here, the large-tree limit is a theoretical scenario where the number of taxa increases to infinity and we can observe the trait values for all species. Specifically, we prove that the MLE converges to the true value under some regularity conditions. These conditions ensure that the tree shape is not too irregular, and holds for many practical scenarios such as trees with bounded edges, trees generated from the Yule (pure birth) process, and trees generated from the coalescent point process. Our result also provides an upper bound for the distance between the MLE and the true value.

Fluorescence determination of the activity of O6-methylguanine-DNA methyltransferase based on the activation of restriction endonuclease and the use of graphene oxide.

A fluorescence method is described for the determination of the activity of O6-methylguanine-DNA methyltransferase (MGMT). It is based on the activation of restriction endonuclease PvuII and the adsorbing a fluorophore-labelled DNA onto the surface of graphene oxide (GO). MGMT activity removes the methyl group from O6-methylguanine (O6MeG) in the fluorophore-labelled DNA to unblock the specific recognition site for further hydrolysis reaction of restriction endonuclease PvuII. The endonuclease catalytic reaction releases fluorophores (5-carboxyfluorescein) from fluorophore-labelled DNA, which can avoid fluorescence quenching by GO, creating an abundance of the fluorescence signal. The fluorescence increase in the assay is thus directly dependent on the MGMT activity. Under the optimal conditions with the emission wavelength of 519 nm (exitation at 494 nm), the activity of the MGMT can be determined in the range 0.5 to 35 ng mL-1 with a detection limit of 0.15 ng mL-1. This is extremely sensitive for the determination of MGMT. The short time of analysis (2 h) is superior to many reported strategies. The method can also be extended for the rapid and sensitive activity assay of other DNA repair enzymes by designing a proper substrate DNA. Conceivably, the technique represents a powerful tool for diagnosis and drug exploitation. Graphical abstract Schematic representation of the fluorescence method for MGMT activity assay.

A Surrogate Function for One-Dimensional Phylogenetic Likelihoods.

Phylogenetics has seen a steady increase in data set size and substitution model complexity, which require increasing amounts of computational power to compute likelihoods. This motivates strategies to approximate the likelihood functions for branch length optimization and Bayesian sampling. In this article, we develop an approximation to the 1D likelihood function as parametrized by a single branch length. Our method uses a four-parameter surrogate function abstracted from the simplest phylogenetic likelihood function, the binary symmetric model. We show that it offers a surrogate that can be fit over a variety of branch lengths, that it is applicable to a wide variety of models and trees, and that it can be used effectively as a proposal mechanism for Bayesian sampling. The method is implemented as a stand-alone open-source C library for calling from phylogenetics algorithms; it has proven essential for good performance of our online phylogenetic algorithm sts.

Multi-task learning improves ancestral state reconstruction.

We consider the ancestral state reconstruction problem where we need to infer phenotypes of ancestors using observations from present-day species. For this problem, we propose a multi-task learning method that uses regularized maximum likelihood to estimate the ancestral states of various traits simultaneously. We then show both theoretically and by simulation that this method improves the estimates of the ancestral states compared to the maximum likelihood method. The result also indicates that for the problem of ancestral state reconstruction under the Brownian motion model, the maximum likelihood method can be improved.

Online Bayesian Phylogenetic Inference: Theoretical Foundations via Sequential Monte Carlo.

Phylogenetics, the inference of evolutionary trees from molecular sequence data such as DNA, is an enterprise that yields valuable evolutionary understanding of many biological systems. Bayesian phylogenetic algorithms, which approximate a posterior distribution on trees, have become a popular if computationally expensive means of doing phylogenetics. Modern data collection technologies are quickly adding new sequences to already substantial databases. With all current techniques for Bayesian phylogenetics, computation must start anew each time a sequence becomes available, making it costly to maintain an up-to-date estimate of a phylogenetic posterior. These considerations highlight the need for an online Bayesian phylogenetic method which can update an existing posterior with new sequences. Here, we provide theoretical results on the consistency and stability of methods for online Bayesian phylogenetic inference based on Sequential Monte Carlo (SMC) and Markov chain Monte Carlo. We first show a consistency result, demonstrating that the method samples from the correct distribution in the limit of a large number of particles. Next, we derive the first reported set of bounds on how phylogenetic likelihood surfaces change when new sequences are added. These bounds enable us to characterize the theoretical performance of sampling algorithms by bounding the effective sample size (ESS) with a given number of particles from below. We show that the ESS is guaranteed to grow linearly as the number of particles in an SMC sampler grows. Surprisingly, this result holds even though the dimensions of the phylogenetic model grow with each new added sequence.

Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals.

Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this article, we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop "guided" proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.

Interfacial Behavior of Oligo(Ethylene Glycol) Dendrons Spread Alone and in Combination with a Phospholipid as Langmuir Monolayers at the Air/Water Interface.

Dendrons consisting of two phosphonate functions and three oligo(ethylene glycol) (OEG) chains grafted on a central phenoxyethylcarbamoylphenoxy group were synthesized and investigated as Langmuir monolayers at the surface of water. The OEG chain in the para position was grafted with a t-Bu end-group, a hydrocarbon chain, or a partially fluorinated chain. These dendrons are models of structurally related OEG dendrons that were found to significantly improve the stability of aqueous dispersions of iron oxide nanoparticles when grafted on their surface. Compression isotherms showed that all OEG dendrons formed liquid-expanded Langmuir monolayers at large molecular areas. Further compression led to a transition ascribed to the solubilization of the OEG chains in the aqueous phase. Brewster angle microscopy (BAM) provided evidence that the dendrons fitted with hydrocarbon chains formed liquid-expanded monolayers throughout compression, whilst those fitted with fluorinated end-groups formed crystalline-like domains, even at large molecular areas. Dimyristoylphosphatidylcholine and dendron molecules were partially miscible in monolayers. The deviations to ideality were larger for the dendrons fitted with a fluorocarbon end-group chain than for those fitted with a hydrocarbon chain. Brewster angle microscopy and atomic force microscopy supported the view that the dendrons were ejected from the phospholipid monolayer during the OEG conformational transition and formed crystalline domains on the surface of the monolayer.

Consistency and convergence rate of phylogenetic inference via regularization.

It is common in phylogenetics to have some, perhaps partial, information about the overall evolutionary tree of a group of organisms and wish to find an evolutionary tree of a specific gene for those organisms. There may not be enough information in the gene sequences alone to accurately reconstruct the correct "gene tree." Although the gene tree may deviate from the "species tree" due to a variety of genetic processes, in the absence of evidence to the contrary it is parsimonious to assume that they agree. A common statistical approach in these situations is to develop a likelihood penalty to incorporate such additional information. Recent studies using simulation and empirical data suggest that a likelihood penalty quantifying concordance with a species tree can significantly improve the accuracy of gene tree reconstruction compared to using sequence data alone. However, the consistency of such an approach has not yet been established, nor have convergence rates been bounded. Because phylogenetics is a non-standard inference problem, the standard theory does not apply. In this paper, we propose a penalized maximum likelihood estimator for gene tree reconstruction, where the penalty is the square of the Billera-Holmes-Vogtmann geodesic distance from the gene tree to the species tree. We prove that this method is consistent, and derive its convergence rate for estimating the discrete gene tree structure and continuous edge lengths (representing the amount of evolution that has occurred on that branch) simultaneously. We find that the regularized estimator is "adaptive fast converging," meaning that it can reconstruct all edges of length greater than any given threshold from gene sequences of polynomial length. Our method does not require the species tree to be known exactly; in fact, our asymptotic theory holds for any such guide tree.

Immunoreaction-Mediated Aggregation of Gold Nanoparticles for Sensitive and Selective Assay of Hepatitis B Surface Antigen.

A rapid, sensitive and quantitative assay method for Hepatitis B surface antigen (HBsAg) is of paramount importance for the drug development and in the diagnosis of this disease. Here, we proposed a novel biosensor that sensitively and selectively screen Hepatitis B surface antigen. This strategy relies on the cross-linking aggregation of gold nanoparticles (AuNPs) that were decorated with Hepatitis B surface antibody (HBsAb) and Raman reporter 5-thio-nitrobenzoic acid (TNB) by Hepatitis B surface antigen. The immune reaction between HBsAb and HBsAg offers this strategy high specificity, and the use of AuNPs additionally allows a visual and homogeneous assay format, thus permitting improved simplicity and throughput of the assays. The selectivity and sensitivity in HBsAg assay were achieved with a wide linear response range from 0.5 ng/mL to 50 ng/mL and a detection limit of 0.2 ng/mL. The results indicated that this strategy can offer a simple, robust and convenient platform for HBsAg analysis and related biochemical studies with high sensitivity and selectivity.

THE SHAPE OF THE ONE-DIMENSIONAL PHYLOGENETIC LIKELIHOOD FUNCTION.

By fixing all parameters in a phylogenetic likelihood model except for one branch length, one obtains a one-dimensional likelihood function. In this work, we introduce a mathematical framework to characterize the shapes of such one-dimensional phylogenetic likelihood functions. This framework is based on analyses of algebraic structures on the space of all frequency patterns with respect to a polynomial representation of thspace likelihood functions. Using this framework, we provide conditions under which the one-dimensional phylogenetic likelihood functions are guaranteed to have at most one stationary point, and this point is the maximum likelihood branch length. These conditions are satisfied by common simple models including all binary models, the Jukes-Cantor model and the Felsenstein 1981 model. We then prove that for the simplest model that does not satisfy our conditions, namely, the Kimura 2-parameter model, the one-dimensional likelihood functions may have multiple stationary points. As a proof of concept, we construct a non-degenerate example in which the phylogenetic likelihood function has two local maxima and a local minimum. To construct such examples, we derive a general method of constructing a tree and sequence data with a specified frequency pattern at the root. We then extend the result to prove that the space of all rescaled and translated one-dimensional phylogenetic likelihood functions under the Kimura 2-parameter model is dense in the space of all non-negative continuous functions on [0, ∞) with finite limits. These results indicate that one-dimensional likelihood functions under advanced evolutionary models can be more complex than it is typically assumed by phylogenetic inference algorithms; however, these complexities can be effectively captured by the Kimura 2-parameter model.

Rhodium(II)-Catalyzed Isomerization of Cyclopropenylmethyl Esters into (Acyloxymethylene)cyclopropanes.

In the presence of a rhodium(II) catalyst, 3,3-disubstituted cyclopropenylmethyl esters that possess an electron-rich or neutral aromatic group undergo isomerization into (acyloxymethylene)cyclopropanes. This transformation, which proceeds with inversion of configuration at the stereogenic center, complements the previously disclosed rearrangement reactions of cyclopropenylmethyl esters. The products arising from this new rhodium-catalyzed rearrangement contain an enol ester group that can be subsequently functionalized to access stereodefined arylcyclopropanes.

Experimental design for dynamics identification of cellular processes.

We address the problem of using nonlinear models to design experiments to characterize the dynamics of cellular processes by using the approach of the Maximally Informative Next Experiment (MINE), which was introduced in W. Dong et al. (PLoS ONE 3(8):e3105, 2008) and independently in M.M. Donahue et al. (IET Syst. Biol. 4:249-262, 2010). In this approach, existing data is used to define a probability distribution on the parameters; the next measurement point is the one that yields the largest model output variance with this distribution. Building upon this approach, we introduce the Expected Dynamics Estimator (EDE), which is the expected value using this distribution of the output as a function of time. We prove the consistency of this estimator (uniform convergence to true dynamics) even when the chosen experiments cluster in a finite set of points. We extend this proof of consistency to various practical assumptions on noisy data and moderate levels of model mismatch. Through the derivation and proof, we develop a relaxed version of MINE that is more computationally tractable and robust than the original formulation. The results are illustrated with numerical examples on two nonlinear ordinary differential equation models of biomolecular and cellular processes.

Effect of coating time on the formation of coating layer and degradation behavior of hydroxyapatite coated ZK60 alloy.

OBJECTIVES: This study aims to investigate the effect of coating time on the formation of hydroxyapatite (HA) coating layer on ZK60 substrate and understand the biodegradation behavior of the coated alloy for biodegradable implant applications. METHODS: Biodegradable ZK60 alloy was coated by HA layer for different times of 0.5, 1, 2, and 4 h by chemical conversion method. After coating, all the coated specimens were used for immersion test in Hanks' solution to understand the effect of coating time on the degradation behavior of the alloy. The degradation rate of the coated alloy was evaluated by Mg2+ ion quantification and pH change during immersion test. The microstructure of the coating layer was examined by scanning electron microscope (SEM) equipped with an energy-dispersive X-ray spectroscopy (EDS) before and after immersion to understand the degradation behavior of the coated alloy. RESULTS: HA coating layers were formed successfully on surface of ZK60 specimens after 0.5, 1, 2, and 4 h with different microstructure. Optimal coating quality was observed at 1 or 2 h, characterized by well-formed and uniform HA layers. However, extending the coating duration to 4 h led to the formation of cracks within the HA layer, accompanied by Mg(OH)2. Specimens coated for 1 and 2 h exhibited the lowest degradation rates, while specimens coated for 0.5 and 4 h showed the highest degradation rates. Furthermore, analysis of degradation products revealed the predominance of calcium phosphates formed on the surface of specimens coated for 1 and 2 h. Conversely, specimens coated for 0.5 and 4 h exhibited Mg(OH)2 as the primary degradation product, suggesting a less effective corrosion barrier under these conditions. CONCLUSION: The HA layer formed after 2 h demonstrated as the most effective coating layer for enhancing the corrosion resistance of the ZK60 alloy for biomedical applications.