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For the use of a gas aggregation cluster source a high and stable deposition rate is desired. For many metals, nanoparticle formation is enhanced by admixture of reactive gases. Here, the role of reactive gas admixtures on the nanoparticle deposition rates is investigated for the case of reactive direct current magnetron sputtering of Ti in a gas aggregation chamber. The results show that, at low working gas (argon) pressures, stable cluster deposition at high rates can only be achieved for admixtures with a very narrow oxygen flow range. At higher pressures, stable deposition can be observed only after an intermediate maximum rate has been crossed or a stable deposition rate is not reached at all. For the different sputtering conditions, the partial pressure of oxygen was monitored with a mass spectrometer. The results are explained in terms of the competing roles of oxygen in cluster nucleation as well as in target poisoning. The cluster size distributions for different conditions were characterized by scanning electron microscopy.

Meisner and Albrechtsen present two methods for inferring population structure and admixture proportions in low depth next-generation sequencing (NGS). NGS methods provide large amounts of genetic data but are associated with statistical uncertainty, especially for low-depth... We here present two methods for inferring population structure and admixture proportions in low-depth next-generation sequencing (NGS) data. Inference of population structure is essential in both population genetics and association studies, and is often performed using principal component analysis (PCA) or clustering-based approaches. NGS methods provide large amounts of genetic data but are associated with statistical uncertainty, especially for low-depth sequencing data. Models can account for this uncertainty by working directly on genotype likelihoods of the unobserved genotypes. We propose a method for inferring population structure through PCA in an iterative heuristic approach of estimating individual allele frequencies, where we demonstrate improved accuracy in samples with low and variable sequencing depth for both simulated and real datasets. We also use the estimated individual allele frequencies in a fast non-negative matrix factorization method to estimate admixture proportions. Both methods have been implemented in the PCAngsd framework available at http://www.popgen.dk/software/.

This study investigated the effect of four volume dosages (that is, 0, 0.1, 0.3, and 0.5%) of high-elastic-modulus carbon microfiber, shrinkage-reducing admixture (SRA), and accelerating admixture (ACC) on the 24-hour compressive strength and restrained shrinkage of carbon microfiber-reinforced concrete. Additional 7-and 28-day compressive strength tests, as well as 1-, 7-, and 28-day splitting tensile strength tests, were carried out on the mixtures without and with 0.3% carbon microfiber. Results showed that, overall, increasing the carbon microfiber dosage increased the compressive strength, particularly at early ages. Splitting tensile strength results were used along with the restrained shrinkage ring results to quantify the restrained shrinkage cracking potential of the mixtures. It was found that carbon microfiber and SRA can both significantly reduce the drying shrinkage cracking potential of concrete. The combination of SRA and ACC in concrete provided compatible effects, characterized by increased early-age compressive strength, as well as reduced shrinkage and cracking potential. Keywords: accelerating admixture; carbon microfiber; early-age strength; fiber-reinforced concrete; restrained shrinkage; shrinkage-reducing admixture.

Abstract Neandertal DNA makes up 2–3% of the genomes of all non-African individuals. The patterns of Neandertal ancestry in modern humans have been used to estimate that this is the result of gene flow that occurred during the expansion of modern humans into Eurasia, but the precise dates of this event remain largely unknown. Here, we introduce an extended admixture pulse model that allows joint estimation of the timing and duration of gene flow. This model leads to simple expressions for both the admixture segment distribution and the decay curve of ancestry linkage disequilibrium, and we show that these two statistics are closely related. In simulations, we find that estimates of the mean time of admixture are largely robust to details in gene flow models, but that the duration of the gene flow can only be recovered if gene flow is very recent and the exact recombination map is known. These results imply that gene flow from Neandertals into modern humans could have happened over hundreds of generations. Ancient genomes from the time around the admixture event are thus likely required to resolve the question when, where, and for how long humans and Neandertals interacted.

Binomial N-mixture models have proven very useful in ecology, conservation, and monitoring: they allow estimation and modeling of abundance separately from detection probability using simple counts. Recently, doubts about parameter identifiability have been voiced. I conducted a large-scale screening test with 137 bird data sets from 2,037 sites. I found virtually no identifiability problems for Poisson and zero-inflated Poisson (ZIP) binomial N-mixture models, but negative-binomial (NB) models had problems in 25% of all data sets. The corresponding multinomial N-mixture models had no problems. Parameter estimates under Poisson and ZIP binomial and multinomial N-mixture models were extremely similar. Identifiability problems became a little more frequent with smaller sample sizes (267 and 50 sites), but were unaffected by whether the models did or did not include covariates. Hence, binomial N-mixture model parameters with Poisson and ZIP mixtures typically appeared identifiable. In contrast, NB mixtures were often unidentifiable, which is worrying since these were often selected by Akaike’s information criterion. Identifiability of binomial N-mixture models should always be checked. If problems are found, simpler models, integrated models that combine different observation models or the use of external information via informative priors or penalized likelihoods, may help.

ObjectiveThere is little evidence that adjuvant therapy after radical surgical resection of hepatocellular carcinoma (HCC) improves recurrence-free survival (RFS) or overall survival (OS). We conducted a multicentre, randomised, controlled, phase IV trial evaluating the benefit of an aqueous extract of Trametes robinophila Murr (Huaier granule) to address this unmet need.Design and resultsA total of 1044 patients were randomised in 2:1 ratio to receive either Huaier or no further treatment (controls) for a maximum of 96 weeks. The primary endpoint was RFS. Secondary endpoints included OS and tumour extrahepatic recurrence rate (ERR). The Huaier (n=686) and control groups (n=316) had a mean RFS of 75.5 weeks and 68.5 weeks, respectively (HR 0.67; 95% CI 0.55 to 0.81). The difference in the RFS rate between Huaier and control groups was 62.39% and 49.05% (95% CI 6.74 to 19.94; p=0.0001); this led to an OS rate in the Huaier and control groups of 95.19% and 91.46%, respectively (95% CI 0.26 to 7.21; p=0.0207). The tumour ERR between Huaier and control groups was 8.60% and 13.61% (95% CI −12.59 to −2.50; p=0.0018), respectively.ConclusionsThis is the first nationwide multicentre study, involving 39 centres and 1044 patients, to prove the effectiveness of Huaier granule as adjuvant therapy for HCC after curative liver resection. It demonstrated a significant prolongation of RFS and reduced extrahepatic recurrence in Huaier group.Trial registration NCT01770431; Post-results.

Most superior cements delivered today contain materials notwithstanding Portland cement to help accomplish the compressive strength or solidness execution. These materials include fly ash, silica fume and ground-granulated blast furnace slag used discretely or in coalescence. Concurrently, chemical admixtures such as high-range di-hydrogen monoxide-reducers are needed to ascertain that the concrete is facile to convey, place and culminate. For high-strength cements, a blend of mineral and compound admixtures is almost consistently fundamental to guarantee accomplishment of the necessary strength. The Primer investigations have been done on concrete, Fine aggregate and coarse aggregate. The Blend Extent for M200 grade concrete is determined 1: 0.313: 1.463 by following the plan methodology given by ACI Strategy. By keeping up the w/c proportion as 0.25, the multi day Compressive strength, Flexural strength and Split elasticity of cement at 3% of silica fume and 1.5% of conplast have been accomplished as 163.33 N/mm2, 8.4 N/mm2& 9.5 N/mm2 separately. The variety of solidarity of cement with the variety of silica fume is appeared in bar outline. The strength of the concrete might be as yet expanded by decreasing the w/c proportion and expanding the level of silica fume

In order to increase the suitability of coarse recycled concrete aggregates and improve the frost resistance of recycled coarse aggregate concrete, this study aims to investigate the effects of an antifreeze-type water-reducing admixture, air-entraining admixture, and antifreeze admixture on the frost resistance of recycled coarse aggregate concrete. The effectiveness of these admixtures is gauged by the mass loss rate and the relative dynamic modulus of elasticity (RDM). Mercury-impressed porosimetry (MIP), super depth of field microscopy, and scanning electron microscopy (SEM) were employed to characterize the hydration products, microstructure, and pore structure of recycled coarse aggregate concrete, with a view to establishing a connection between the microstructural characteristics and the macro properties and analyzing the micro-mechanism of the improvement effect of frost resistance. The test results demonstrate that the admixtures have a significant impact on the frost resistance of recycled coarse aggregate concrete. In particular, the recycled coarse aggregate concrete with an antifreeze admixture (dosage of 1%) and a water–cement ratio of 0.41 exhibited a mass loss of only 1.23% after 200 freezing and thawing cycles, a relative dynamic modulus of elasticity of up to 93.97%; however, the control group had reached the stopping condition at 150 freeze–thaw cycles with more than 10% mass loss. The recycled coarse aggregate concrete with added antifreeze admixture had a tight connection between the aggregate and the paste and a more pronounced improvement in the pore structure, indicating excellent resistance to frost damage.

To investigate the effects of viscosity-modifying admixture (VMA) on the extrudability of limestone and calcined clay-based cementitious materials, three mix designs with different dosages of VMA were proposed in this study. The ram extrusion was utilized as an extrusion model for exploring the fresh properties of printable materials. Two methods were used, based on the ram extruder setup—(a) extruding materials with the same extrusion speed at different rest times to determine how the pressure changes with time; (b) extruding materials with different extrusion speeds at the same rest time to investigate the material flow parameters using the Basterfield et al. model. The main findings of this study could be summarized as—(1) the extrusion pressure of all mix designs exhibited an increasing trend with time. At the same tested age, the extrusion pressure under 0.25 mm/s of piston speed was increased and the shape retention of the extruded filaments was enhanced by increasing the dosage of VMA; (2) the correlation between the experimental results and the Basterfield et al. model was excellent (R-squared value: 0.99). The mixture with a higher content of VMA showed an increased elongational yield stress, flow consistency, and shear yield stress.

Mixture of experts (ME) is one of the most popular and interesting combining methods, which has great potential to improve performance in machine learning. ME is established based on the divide-and-conquer principle in which the problem space is divided between a few neural network experts, supervised by a gating network. In earlier works on ME, different strategies were developed to divide the problem space between the experts. To survey and analyse these methods more clearly, we present a categorisation of the ME literature based on this difference. Various ME implementations were classified into two groups, according to the partitioning strategies used and both how and when the gating network is involved in the partitioning and combining procedures. In the first group, The conventional ME and the extensions of this method stochastically partition the problem space into a number of subspaces using a special employed error function, and experts become specialised in each subspace. In the second group, the problem space is explicitly partitioned by the clustering method before the experts' training process starts, and each expert is then assigned to one of these sub-spaces. Based on the implicit problem space partitioning using a tacit competitive process between the experts, we call the first group the mixture of implicitly localised experts (MILE), and the second group is called mixture of explicitly localised experts (MELE), as it uses pre-specified clusters. The properties of both groups are investigated in comparison with each other. Investigation of MILE versus MELE, discussing the advantages and disadvantages of each group, showed that the two approaches have complementary features. Moreover, the features of the ME method are compared with other popular combining methods, including boosting and negative correlation learning methods. As the investigated methods have complementary strengths and limitations, previous researches that attempted to combine their features in integrated approaches are reviewed and, moreover, some suggestions are proposed for future research directions.[PUBLICATION ABSTRACT]