A Soft Spot for Chemistry–Current Taxonomic and Evolutionary Implications of Sponge Secondary Metabolite Distribution

Marine sponges are the most prolific marine sources for discovery of novel bioactive compounds. Sponge secondary metabolites are sought-after for their potential in pharmaceutical applications, and in the past, they were also used as taxonomic markers alongside the difficult and homoplasy-prone sponge morphology for species delineation (chemotaxonomy). The understanding of phylogenetic distribution and distinctiveness of metabolites to sponge lineages is pivotal to reveal pathways and evolution of compound production in sponges. This benefits the discovery rate and yield of bioprospecting for novel marine natural products by identifying lineages with high potential of being new sources of valuable sponge compounds. In this review, we summarize the current biochemical data on sponges and compare the metabolite distribution against a sponge phylogeny. We assess compound specificity to lineages, potential convergences, and suitability as diagnostic phylogenetic markers. Our study finds compound distribution corroborating current (molecular) phylogenetic hypotheses, which include yet unaccepted polyphyly of several demosponge orders and families. Likewise, several compounds and compound groups display a high degree of lineage specificity, which suggests homologous biosynthetic pathways among their taxa, which identifies yet unstudied species of this lineage as promising bioprospecting targets.

Recurrent Coupled Topic Modeling over Sequential Documents

The abundant sequential documents such as online archival, social media, and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and their temporal dependencies. However, most of the existing approaches focus on single-topic-thread evolution and ignore the fact that a current topic may be coupled with multiple relevant prior topics. In addition, these approaches also incur the intractable inference problem when inferring latent parameters, resulting in a high computational cost and performance degradation. In this work, we assume that a current topic evolves from all prior topics with corresponding coupling weights, forming the multi-topic-thread evolution . Our method models the dependencies between evolving topics and thoroughly encodes their complex multi-couplings across time steps. To conquer the intractable inference challenge, a new solution with a set of novel data augmentation techniques is proposed, which successfully discomposes the multi-couplings between evolving topics. A fully conjugate model is thus obtained to guarantee the effectiveness and efficiency of the inference technique. A novel Gibbs sampler with a backward–forward filter algorithm efficiently learns latent time-evolving parameters in a closed-form. In addition, the latent Indian Buffet Process compound distribution is exploited to automatically infer the overall topic number and customize the sparse topic proportions for each sequential document without bias. The proposed method is evaluated on both synthetic and real-world datasets against the competitive baselines, demonstrating its superiority over the baselines in terms of the low per-word perplexity, high coherent topics, and better document time prediction.

Elevation Calculation of Bottom Deck Based on Stochastic Process and Compound Distribution

In the design of offshore platforms, the height of the bottom deck directly affects the safety and engineering cost of the entire platform. It is a very important scale parameter in platform planning. The American Petroleum Institute (API) specification shows that the key to determining the height of the bottom deck lies in the wave height and calculation of the return level of the water increase. Based on the perspective of stochastic processes, this paper constructs a new distribution function model for joint parameter estimation of the marine environment. The new model uses a family of random variables to show the statistical characteristics of design wave height and water increase in both time and space, with extreme value expanded EED-I type distribution used as marginal distribution. The new model performs statistical analysis on the measured hydrological data of the Naozhou Station during the flood period from 1990 to 2016. The Gumbel–Copula structure function is used as the connection function, and the compound distribution model of the wave height and the water increase is used to obtain the joint return level of the wave height and the water increase and through which the bottom deck height of the area is calculated. The results show that the stochastic compound distribution improves the issue of the high design value caused by simple superposition of univariate return levels. The EED-I type distribution still has good stability under the condition of less measured data. Thus, under the premise of ensuring the safety of the offshore platform, less measured data can still be used to calculate the height of the bottom deck more accurately.

Conversion of Oil Palm Empty Fruit Bunch (EFB) Biomass to Bio-Oil and Jet Bio-Fuel by Catalytic Fast-Pyrolysis Process

Oil palm empty fruit bunch (EFB) biomass is a potential source of renewable energy. Catalytic fast-pyrolysis batch process was initially performed to convert oil palm EFB into bio-oil, followed by its refinement to jet bio-fuel. Crystalline zeolites A and Y; synthesised from rice husk ash (RHA), were applied as heterogeneous catalysts. The catalytic conversion of oil palm EFB to bio-oil was conducted at a temperature range of 320-400°C with zeolite A catalyst loadings of 0.6 - 3.0 wt%. The zeolite catalysts were characterised by XRD, FTIR and FESEM. The bio-oil and jet bio-fuel products were analysed using GC-MS and FTIR. The batch fast-pyrolysis reaction was optimised at 400°C with a catalyst loading of 1.0 wt%, produced 42.7 wt% yields of liquid bio-oil, 35.4 wt% char and 21.9 wt% gaseous products. Analysis by GCMS indicates the compound distribution of the liquid bio-oil are as follows: hydrocarbons (23%), phenols (61%), carboxylic acids (0.7%), ketones (2.7%), FAME (7.7%) and alcohols (0.8%). Further refinement of the liquid bio-oil by catalytic hydrocracking over zeolite Y produced jet bio-fuel, which contains 63% hydrocarbon compounds (C8-C18) and 16% of phenolic compounds.

Computational Linguistic Assessment of Textbooks and Online Texts by Means of Threshold Concepts in Economics

The ongoing digitalization of educational resources and the use of the internet lead to a steady increase of potentially available learning media. However, many of the media which are used for educational purposes have not been designed specifically for teaching and learning. Usually, linguistic criteria of readability and comprehensibility as well as content-related criteria are used independently to assess and compare the quality of educational media. This also holds true for educational media used in economics. This article aims to improve the analysis of textual learning media used in economic education by drawing on threshold concepts. Threshold concepts are key terms in knowledge acquisition within a domain. From a linguistic perspective, however, threshold concepts are instances of specialized vocabularies, exhibiting particular linguistic features. In three kinds of (German) resources, namely in textbooks, in newspapers, and on Wikipedia, we investigate the distributive profiles of 63 threshold concepts identified in economics education (which have been collected from threshold concept research). We looked at the threshold concepts' frequency distribution, their compound distribution, and their network structure within the three kinds of resources. The two main findings of our analysis show that firstly, the three kinds of resources can indeed be distinguished in terms of their threshold concepts' profiles. Secondly, Wikipedia definitely shows stronger associative connections between economic threshold concepts than the other sources. We discuss the findings in relation to adequate media use for teaching and learning—not only in economic education.

On a Generalised Exponential-Lindley Mixture of Generalised Poisson Distribution

Background: A mixture distribution arises when some or all parameters in a mixing distribution vary according to the nature of original distribution. A generalised exponential-Lindley distribution (GELD) was obtained by Mishra and Sah (2015). In this paper, generalized exponential- Lindley mixture of generalised Poisson distribution (GELMGPD) has been obtained by mixing generalised Poisson distribution (GPD) of Consul and Jain’s (1973) with GELD. In the proposed distribution, GELD is the original distribution and GPD is a mixing distribution. Generalised exponential- Lindley mixture of Poisson distribution (GELMPD) was obtained by Sah and Mishra (2019). It is a particular case of GELMGPD. Materials and Methods: GELMGPD is a compound distribution obtained by using the theoretical concept of some continuous mixtures of generalised Poisson distribution of Consul and Jain (1973). In this mixing process, GELD plays a role of original distribution and GPD is considered as mixing distribution. Results: Probability mass of function (pmf) and the first four moments about origin of the generalised exponential-Lindley mixture of generalised Poisson distribution have been obtained. The method of moments has been discussed to estimate parameters of the GELMGPD. This distribution has been fitted to a number of discrete data-sets which are negative binomial in nature. P-value of this distribution has been compared to the PLD of Sankaran (1970) and GELMPD of Sah and Mishra (2019) for similar type of data-sets. Conclusion: It is found that P-value of GELMGPD is greater than that in each case of PLD and GELMPD. Hence, it is expected to be a better alternative to the PLD of Sankaran and GELMPD of Sah and Mishra for similar types of discrete data-sets which are negative binomial in nature. It is also observed that GELMGPD gives much more significant result when the value of is negative.

A Gini approach to spatial CO2 emissions

Combining global gridded population and fossil fuel based CO2 emission data at 1 km scale, we investigate the spatial origin of CO2 emissions in relation to the population distribution within countries. We depict the correlations between these two datasets by a quasi-Lorenz curve which enables us to discern the individual contributions of densely and sparsely populated regions to the national CO2 emissions. We observe pronounced country-specific characteristics and quantify them using an indicator resembling the Gini-index. As demonstrated by a robustness test, the Gini-index for each country arise from a compound distribution between the population and emissions which differs among countries. Relating these indices with the degree of socio-economic development measured by per capita Gross Domestic Product (GDP) at purchase power parity, we find a strong negative correlation between the two quantities with a Pearson correlation coefficient of -0.71. More specifically, this implies that in developing countries locations with large population tend to emit relatively more CO2, and in developed countries the opposite tends to be the case. Based on the relation to urban scaling, we discuss the implications for CO2 emissions from cities. Our results show that general statements with regard to the (in)efficiency of large cities should be avoided as it is subject to the socio-economic development of respective countries. Concerning the political relevance, our results suggest a differentiated spatial prioritization in deploying climate change mitigation measures in cities for developed and developing countries.