关注早产儿救治的生命伦理学问题

随着围生医学的发展,早产儿的存活率有了明显的提高,但对于生存极限或危重症早产儿是否救治等伦理学问题值得临床医生关注。在该过程中,家长及医护人员的救治态度对于患儿的存活情况影响较大,因此,良好的医患沟通及医院伦理委员会的成立对提高小早产儿存活率、减少医患矛盾具有不容忽视的作用。     

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal that is difficult to pick up with small sample sizes. Lastly, we use our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing data set of tens of thousands of individuals assayed at a few hundred genic regions.The demography of an evolving population strongly influences the genetic variation found within it, and understanding the intricate interplay between natural selection, genetic drift, and demography is a key aim of population genomics. For example, the human census population has expanded more than 1000-fold in the last 400 generations (Keinan and Clark 2012), resulting in a state that is profoundly out of equilibrium with respect to genetic variation. Recently, there has been much interest in studying the consequences of such rapid expansion on mutation load and the genetic architecture of complex traits (Gazave et al. 2013; Lohmueller 2014; Simons et al. 2014). Estimating the population demography is necessary for developing more accurate null models of neutral evolution in order to identify genomic regions subject to natural selection (Williamson et al. 2005; Boyko et al. 2008; Lohmueller et al. 2008). The problem of inferring demography from genomic data also has several other important applications. In particular, the population demography is needed to correct for spurious genotype-phenotype associations in genome-wide association studies due to hidden population substructure (Marchini et al. 2004; Campbell et al. 2005; Clayton et al. 2005), to date historical population splits, migrations, admixture, and introgression events (Gravel et al. 2011; Li and Durbin 2011; Lukić and Hey 2012; Sankararaman et al. 2012), to compute random match probabilities accurately in forensic applications (Balding and Nichols 1997; Graham et al. 2000), for examples.A commonly used null model in population genetics assumes that individuals are randomly sampled from a well-mixed population of constant size that evolves neutrally according to some model of random mating (Ewens 2004). However, several recent large-sample sequencing studies in humans (Coventry et al. 2010; Fu et al. 2012; Nelson et al. 2012; Tennessen et al. 2012) have found an excess of single nucleotide variants (SNVs) that have very low minor allele frequency (MAF) in the sample compared to that predicted by coalescent models with a constant effective population size. For example, in a sample of ∼12,500 individuals of European descent analyzed by Nelson et al. (2012), >74% of the SNVs have only one or two copies of the minor allele, and >95% of the SNVs have an MAF <0.5%. On the other hand, assuming a constant population size over time, Kingman’s coalescent predicts that the number of neutral SNVs is inversely proportional to the sample frequency of the variant (Fu 1995). Keinan and Clark (2012) have suggested that such an excess of sites segregating with low MAF can be explained by recent exponential population growth. In particular, a rapid population expansion produces genealogical trees that have long branch lengths at the tips of the trees, leading to a large fraction of mutations being limited to a single individual in the sample. Motivated by these findings and rapidly increasing sample sizes in population genomics, we here tackle the problem of developing an efficient algorithm for inferring historical effective population sizes and locus-specific mutation rates using a very large sample, with tens or hundreds of thousands of individuals.At the coarsest level, previous approaches to inferring demography from genomic variation data can be divided according to the representation of the data that they operate on. Full sequence-based approaches for inferring the historical population size such as the works of Li and Durbin (2011) and Sheehan et al. (2013) use between two and a dozen genomes to infer piecewise constant models of historical population sizes. Since these approaches operate genome wide, they can take into account linkage information between neighboring SNVs. On the other hand, they are computationally very expensive and cannot be easily applied to infer recent demographic events from large numbers of whole genomes. A slightly more tractable approach to inferring potentially complex demographies involves comparing the length distribution of identical-by-descent and identical-by-state tracts between pairs of sequences (Palamara et al. 2012; Harris and Nielsen 2013).The third class of methods, and the one that our approach also belongs to, summarizes the variation in the genome sequences by the sample frequency spectrum (SFS). The SFS of a sample of size n counts the number of SNVs as a function of their mutant allele frequency in the sample. Since the SFS is a very efficient dimensional reduction of large-scale population genomic data that summarizes the variation in n sequences by n − 1 numbers, it is naturally attractive for computational and statistical purposes. Furthermore, the expected SFS of a random sample drawn from the population strongly depends on the underlying demography, and there have been several previous approaches that exploit this relationship for demographic inference. Nielsen (2000) developed a method based on coalescent tree simulations to infer exponential population growth from single nucleotide polymorphisms that are far enough apart to be in linkage equilibrium. Coventry et al. (2010) developed a similar coalescent simulation-based method that additionally infers per-locus mutation rates and applied this method to exome-sequencing data from ∼10,000 individuals at two genes. Nelson et al. (2012) have also applied this method to a larger data set of 11,000 individuals of European ancestry (CEU) sequenced at 185 genes to infer a recent epoch of exponential population growth. The common feature of all these methods is that they use Monte Carlo simulations to empirically estimate the expected SFS under a given demographic model, and then they compute a pseudo-likelihood function for the demographic model by comparing the expected and observed SFS. The optimization over the demographic models is then performed via grid search procedures. More recently, Excoffier et al. (2013) have developed a software package that employs coalescent tree simulations to estimate the expected joint SFS of multiple subpopulations for inferring potentially very complex demographic scenarios from multipopulation genomic data. The problem of demographic inference has also been approached from the perspective of diffusion processes. Given a demographic model, one can derive a partial differential equation (PDE) for the density of segregating sites at a given derived allele frequency as a function of time. Gutenkunst et al. (2009) used numerical methods to approximate the solution to this PDE, while Lukić et al. (2011) approximated this solution using an orthogonal polynomial expansion. The coalescent-based method of Excoffier et al. (2013), fastsimcoal, and the diffusion-based method of Gutenkunst et al. (2009), ∂a∂i, can infer the joint demography of multiple subpopulations with changing population sizes and complex patterns of migration between subpopulations.In this paper, we focus on the problem of inferring the effective population size as a function of time for a single randomly mating population. As mentioned above, our method is based on the SFS. By restricting our inference to a single population, we are able to compute the expected SFS exactly, rather than using Monte Carlo simulations or solving PDEs numerically. Briefly, we utilize the theoretical work of Polanski et al. (2003) and Polanski and Kimmel (2003), which relate the expected SFS for a sample of size n from a single population to the expected waiting times to the first coalescence event for all sample sizes ≤n. We show that the latter quantities can be computed efficiently and numerically stably for very large sample sizes and for an arbitrary piecewise-exponential model of the historical effective population size. Further, our method utilizes the technique of automatic differentiation to compute exact gradients of the likelihood with respect to the parameters of the effective population size function, thereby facilitating optimization over the space of demographic parameters. These techniques result in our method being both more accurate and more computationally efficient than ∂a∂i and fastsimcoal. In what follows, we carry out an extensive simulation study to demonstrate that our method can infer multiple recent epochs of rapid exponential growth and estimate locus-specific mutation rates with a high accuracy. We then apply our method to analyze data from recent sequencing studies.     

Illustration of charge transfer in graphene-coated hexagonal ZnO photocatalysts using Kelvin probe force microscopy

A graphene coated hexagonal ZnO (HZO@Gr) with enhanced activity in photocatalysis was synthesized. However, the photoinduced charge transfer behavior and the beneficial role of graphene in promoting photocatalytic reactions have not been sufficiently investigated experimentally. In this paper, the surface potentials of the ±(0001)-polar plane of HZO (Zn-polar plane and O-polar plane), graphene, graphene/Zn-polar plane and graphene/O-polar plane were measured using Kelvin probe force microscopy (KPFM). On the basis of the KPFM results, the respective Fermi levels were calculated and the internal electric field (IEF) of HZO was confirmed. Taking the IEF of HZO into consideration, the three-dimensional band diagrams of the HZO@Gr composites in methyl blue (MB) solution in the dark and under UV-visible irradiation after equilibrium were proposed. Accordingly, it is found that there could emerge different interactions between graphene and HZO at the ±(0001)-polar plane of HZO. Furthermore, the photogenerated holes and electrons tend to migrate to opposite directions. With the participation of graphene and IEF, the composites show a decrease in possibility of charge recombination. As a result, the active groups, namely ˙OH and ˙O₂⁻ radicals, could be mainly generated at/near the O-polar plane and Zn-polar plane, respectively. This work can serve as a supplemental explanation of the charge transfer during the photocatalytic process at the polar ZnO/graphene composite surface.

The Fermi levels and three-dimensional band diagrams of the synthesized HZO@Gr composites in methyl blue (MB) solution before and after equilibrium were assumed.     

Corrosion behavior of 904L austenitic stainless steel in hydrofluoric acid

Corrosion behaviors of 904L austenitic stainless steel in HF and HCl were studied and compared using electrochemical method, microscopic analysis, and phase analysis. An insoluble layer is deposited on 904L in HF due to a preferential reaction between [F⁻] and the [Ni] from the alloy. This insoluble deposited layer not only helps isolate the aggressive ions from the base metal, but also inhibits the passivation of 904L in HF, the mechanism for which was entirely different from that in HCl.

An insoluble layer is deposited on 904L in HF due to a preferential reaction between [F⁻] and the [Ni] from the alloy. This insoluble deposit layer helps isolate the aggressive ions from the base metal, while inhibits the passivation of 904L in HF.     

Fabrication of a hyaluronic acid conjugated metal organic framework for targeted drug delivery and magnetic resonance imaging

Since metal organic frameworks (MOF) have exhibited fascinating potential in biomedical applications, it is worthwhile to construct a MOF-based multifunctional drug delivery system. In the present study, the anticancer drug doxorubicin (DOX) was loaded into zeolitic imidazolate framework-8 (ZIF-8) via a one-pot process. The formed DOX@ZIF-8 was then coated with polydopamine, successively chelated with Fe³⁺ and conjugated with hyaluronic acid (HA), finally resulting in a multifunctional ZIF-8 nanocarrier. The characterization results confirmed the successful formation of the hybrid nanocarrier. pH-responsive drug release of DOX was observed due to the innate pH-dependent stability of ZIF-8. Importantly, the flow cytometry and confocal laser scanning microscope results both verified the targeting ability of DOX@ZIF-HA toward prostate cancer PC-3 cells. The improved therapeutic efficacy of DOX@ZIF-HA when compared to the inhibited group was also demonstrated. Furthermore, the chelation of Fe³⁺ by PDA makes the prepared DOX@ZIF-HA a good contrast agent for magnetic resonance (MR) imaging. Hence, we hope the constructed ZIF-8 based multifunctional nanocarrier could be a candidate for cancer theranostics.

DOX-doped MOF nanoparticles were prepared via a one-pot reaction and successively anchored with Fe³⁺ and HA for simultaneous targeted drug delivery and MR imaging.     

A short review of recent advances in CO2 hydrogenation to hydrocarbons over heterogeneous catalysts

CO₂ hydrogenation to hydrocarbons is a promising way of making waste to wealth and energy storage, which also solves the environmental and energy issues caused by CO₂ emissions. Much efforts and research are aimed at the conversion of CO₂via hydrogenation to various value-added hydrocarbons, such as CH₄, lower olefins, gasoline, or long-chain hydrocarbons catalyzed by different catalysts with various mechanisms. This review provides an overview of advances in CO₂ hydrogenation to hydrocarbons that have been achieved recently in terms of catalyst design, catalytic performance and reaction mechanism from both experiments and density functional theory calculations. In addition, the factors influencing the performance of catalysts and the first C–C coupling mechanism through different routes are also revealed. The fundamental factor for product selectivity is the surface H/C ratio adjusted by active metals, supports and promoters. Furthermore, the technical and application challenges of CO₂ conversion into useful fuels/chemicals are also summarized. To meet these challenges, future research directions are proposed in this review.

CO₂ hydrogenation to hydrocarbons over heterogeneous catalysts.     

Efficient and reusable SBA-15-immobilized Brønsted acidic ionic liquid for the ketalization of cyclohexanone with glycol

Ketalization of cyclohexanone with glycol has been carried out using molecular sieve SBA-15 immobilized Brønsted acidic ionic liquid catalyst. The properties of the heterogeneous catalysts were characterized by elemental analysis, Fourier transform infrared (FT-IR) spectra, scanning electron microscopy (SEM), thermogravimetry/differential scanning calorimetry (TG/DSC), and N₂ adsorption–desorption (BET). The results suggested that Brønsted acidic ionic liquid [BSmim][HSO₄] had been successfully immobilized on the surface of SBA-15 and the catalytic performance evaluation demonstrated that the catalyst BAIL@SBA-15 exhibited excellent catalytic activities in the ketalization of cyclohexanone with glycol. In addition, the effects of reaction temperature, catalyst loading, reaction time, and reactant molar ratio have also been investigated in detail, and a general reaction mechanism for the ketalization of cyclohexanone with glycol was given. The SBA-15 immobilized ionic liquid can be recovered easily and after reusing for 5 times in the ketalization reaction, the catalyst could still give satisfactory catalytic activity.

Molecular sieve SBA-15 immobilized Brønsted acidic ionic liquid: an efficient and recyclable catalyst for the ketalization of cyclohexanone with glycol.     

Effect and mechanism of oyster hydrolytic peptides on spatial learning and memory in mice

Oysters (Crassostrea talienwhanensis) contain large amounts of protein and exhibit many biological activities. This study was aimed at preparing oyster protein hydrolysates (OPH) and evaluating the OPH based on a spatial learning and memory capacity. A response surface methodology was employed to optimize hydrolysis conditions to determine the OPH with the highest AChE inhibitory activity, and the optimum extraction conditions were as follows: enzyme concentration of 1444.88 U g⁻¹, pH of 7.38, extraction temperature of 45 °C, extraction time of 5.56 h and a water/material ratio of 2.45 : 1, and the minimum acetylcholinesterase (AChE) activity was 0.069 mM min⁻¹. The spatial memory and learning abilities and passive avoidance in mice were determined by using the Morris water maze test and a dark/light avoidance test. Furthermore, the OPH group could relieve oxidative stress, reduce AChE levels, increase choline acetyltransferase (ChAT) levels and alleviate inflammatory reaction through reduction of interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) levels. Additionally, up-regulated expressions of brain-derived neurotrophic factor (BDNF) and neural cell adhesion molecules (NCAM) were observed in mice treated with OPH. These findings suggested that OPH could be a functional food candidate to improve the learning and memory ability associated with oxidative stress and inflammatory reactions.

Oyster protein hydrolysate could be a functional food candidate to improve learning and memory ability.     

3D Pd/Co core–shell nanoneedle arrays as a high-performance cathode catalyst layer for AAEMFCs

A novel cathode architecture using vertically aligned Co nanoneedle arrays as an ordered support for application in alkaline anion-exchange membrane fuel cells (AAEMFCs) has been developed. The Co nanoneedle arrays were directly grown on a stainless steel sheet via a hydrothermal reaction and then a Pd layer was deposited on the surface of the Co nanoneedle arrays using a vacuum sputter-deposition method to form Pd/Co nanoneedle arrays. After transferring the Pd/Co nanoneedle arrays to an AAEM, a cathode catalyst layer was formed. Without the use of an alkaline ionomer, the AAEMFC with the prepared cathode catalyst layer showed an enhanced performance with ultra-low Pd loading of down to 33.5 μg cm⁻², which is much higher than the conventionally used cathode electrode with a Pt loading of 100 μg cm⁻². This is the first report where three-dimensional Co nanoneedle arrays have been used as the cathode support in an AAEMFC, which is able to deliver a higher power density without an alkaline ionomer than that of conventional membrane electrode assembly (MEA).

A novel ordered Pd/Co nanoneedle array was used as a cathode in an AAEMFC and delivered a higher power density than that of conventional MEA.