Metabolomic Analysis Reveals Contributions of Citric and Citramalic Acids to Rare Earth Bioleaching by a Paecilomyces Fungus.

Conventional methods for extracting rare earth elements from monazite ore require high energy inputs and produce environmentally damaging waste streams. Bioleaching offers a potentially more environmentally friendly alternative extraction process. In order to better understand bioleaching mechanisms, we conducted an exo-metabolomic analysis of a previously isolated rare earth bioleaching fungus from the genus Paecilomyces (GenBank accession numbers KM874779 and KM 874781) to identify contributions of compounds exuded by this fungus to bioleaching activity. Exuded compounds were compared under two growth conditions: growth with monazite ore as the only phosphate source, and growth with a soluble phosphate source (K2HPO4) added. Overall metabolite profiling, in combination with glucose consumption and biomass accumulation data, reflected a lag in growth when this organism was grown with only monazite. We analyzed the relationships between metabolite concentrations, rare earth solubilization, and growth conditions, and identified several metabolites potentially associated with bioleaching. Further investigation using laboratory prepared solutions of 17 of these metabolites indicated statistically significant leaching contributions from both citric and citramalic acids. These contributions (16.4 and 15.0 mg/L total rare earths solubilized) accounted for a portion, but not all, of the leaching achieved with direct bioleaching (42 ± 15 mg/L final rare earth concentration). Additionally, citramalic acid released significantly less of the radioactive element thorium than did citric acid (0.25 ± 0.01 mg/L compared to 1.18 ± 0.01 mg/L), suggesting that citramalic acid may have preferable leaching properties for a monazite bioleaching process

A Study of Cold Gas and Star Formation in Low-Mass Blue-Sequence E/S0s

We present a study of cold gas and star formation in low-mass blue-sequence E/S0 galaxies — a population that is morphologically early-type, but resides on the blue sequence in color vs. stellar mass space alongside spirals. A subset of these blue-sequence E/S0s may provide an evolutionary link between traditional red and dead early-type galaxies and star-forming spirals via disk (re)growth. In this dissertation, we use data from the Green Bank Telescope (GBT), the Combined Array for Research in Millimeter-wave Astronomy (CARMA), the Spitzer Space Telescope, and the Galaxy Evolution Explorer (GALEX) to examine the potential for morphological transformation of low-mass blue-sequence E/S0s. In considering the HI content of these galaxies, we find that, normalized to stellar mass, the atomic gas masses for 12 of the 14 blue-sequence E/S0s range from 0.1 to ≥1.0. These gas-to-stellar mass ratios are comparable to those of spiral and irregular galaxies, and have a similar dependence on stellar mass. Assuming that the HI is accessible for star formation, we find that 9 of 14 blue-sequence E/S0s can increase in stellar mass by 10–60% in 3 Gyr with current HI reservoirs alone. We present evidence that star formation in these galaxies is bursty and likely involves externally triggered gas inflows. For a sub-sample of eight E/S0s (four blue-, two mid-, and two red-sequence) whose CARMA CO(1–0), Spitzer MIPS 24μm, and GALEX FUV emission distributions are spatially resolved on a 750pc scale, we find roughly linear relationships between molecular-gas and star-formation surface densities within all galaxies, with power law indices N = 0.6–1.9 (median 1.2). Adding 11 more blue-sequence E/S0s whose CO(1–0) emission is not as well resolved, we find that most of our E/S0s have 1–8 kpc aperture-averaged molecular-gas surface densities overlapping the range spanned by the disks and centers of spiral galaxies. While many of our E/S0s fall on the same Schmidt-Kennicutt relation as local spirals, ∼80% are offset towards apparently higher molecular-gas star formation efficiency. We discuss possible interpretations of the apparently elevated efficiencies. We examine star formation in blue- and red-sequence E/S0s as traced by the 8μm PAH emission. We find the 8μm PAH/3.6μm emission ratios for most of our blue-sequence E/S0s to be similar to those of local spirals. Ratio images of the two tracers reveal ring-like and non-axisymmetric structures in some of our E/S0s, suggestive of internally and/or externally triggered gas inflow and centrally concentrated star formation. Comparison between the CO(1–0) and 8μm PAH emission distributions shows good agreement globally, although the 8μm PAH/3.6μm emission ratio appears to better trace non-axisymmetric structures observed in CO. Similar to CO observations of spiral galaxies, we find detectable CO emission in our E/S0s to be centrally concentrated, ranging from 0.1&ndash0.6r25 (median 0.3r25). We also find that the aperture-averaged 8μm PAH to 3.6μm stellar emission ratio correlates with the atomic and molecular gas mass fractions

A method for assessing the success and failure of community-level interventions in the presence of network diffusion, social reinforcement, and related social effects

Prevention and intervention work done within community settings often face unique analytic challenges for rigorous evaluations. Since community prevention work (often geographically isolated) cannot be controlled in the same way other prevention programs and these communities have an increased level of interpersonal interactions, rigorous evaluations are needed. Even when the `gold standard' randomized control trials are implemented within community intervention work, the threats to internal validity can be called into question given informal social spread of information in closed network settings. A new prevention evaluation method is presented here to disentangle the social influences assumed to influence prevention effects within communities. We formally introduce the method and it's utility for a suicide prevention program implemented in several Alaska Native villages. The results show promise to explore eight sociological measures of intervention effects in the face of social diffusion, social reinforcement, and direct treatment. Policy and research implication are discussed.Comment: 18 pages, 5 figure

Digital Transformations in Taiwanese TV Industry

In the past, TV was always regarded as an indispensable member of every family. Watching TV programs with the whole family was once one of the key consumer behaviors. However, with the development of technology, the digital wave and the invasion of Over-The-Top (OTT)platforms, consumer behavior has begun to undergo drastic changes. Mobile phones and tablets occupy most of our time. Multi-screens have long become the norm. According to the Digital Whirlpool report published by IMD in 2019: Due to the impact of digital convergence, digital disruption has already occurred in the media, entertainment, and telecommunications industries. If digital transformation is not carried out in time, the next five may be replaced by other new services . Observe that the number of cable TV subscribers in Taiwan has dropped from 5.23 million in 2017. With the influence of online platforms and online pirated content, it has fallen all the way to the current low of 4.83 million in 2021.Facing the changes in viewers’ viewing behaviors and the shift in TV advertising budgets in recent years, various TV stations have also provided solutions and actively transformed from internal thinking to external environments. TV stations such as TVBS, Eastern Broadcasting Company (EBC), Sanli TV and Ctitv have begun their digital transformation

Bias detection and correction in RNA-Sequencing data

<p>Abstract</p> <p>Background</p> <p>High throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low background, and ability to identify novel transcripts. Moreover, for genes with multiple isoforms, expression of each isoform may be estimated from RNA-Seq data. Despite these advantages, recent work revealed that base level read counts from RNA-Seq data may not be randomly distributed and can be affected by local nucleotide composition. It was not clear though how the base level read count bias may affect gene level expression estimates.</p> <p>Results</p> <p>In this paper, by using five published RNA-Seq data sets from different biological sources and with different data preprocessing schemes, we showed that commonly used estimates of gene expression levels from RNA-Seq data, such as reads per kilobase of gene length per million reads (RPKM), are biased in terms of gene length, GC content and dinucleotide frequencies. We directly examined the biases at the gene-level, and proposed a simple generalized-additive-model based approach to correct different sources of biases simultaneously. Compared to previously proposed base level correction methods, our method reduces bias in gene-level expression estimates more effectively.</p> <p>Conclusions</p> <p>Our method identifies and corrects different sources of biases in gene-level expression measures from RNA-Seq data, and provides more accurate estimates of gene expression levels from RNA-Seq. This method should prove useful in meta-analysis of gene expression levels using different platforms or experimental protocols.</p