Molecular Phylogeny of Giant Clams Based on Mitochondrial DNA Cytochrome C Oxidase I Gene

There is an uncertainty for the relationships among giant clam species of Tridacninae, in particular among species belongs to subgenus Chametrachea i.e. Tridacna crocea, T. maxima, and T. squamosa based on different genetic markers. This study examined the relationships among three species within subgenus Chametrachea compared to the previous studies. Neighbour Joining, Maximum Parsimony and Maximum Likelihood tree were constructed based on 455 bp of the mitochondrial DNA cytochrome c oxidase I gene from T. crocea, T. squamosa, T. maxima, T. gigas, and several sequences derived from Genbank for the outgroups. The results showed that giant clams formed a monophyletic group. Within Tridacna group, T. crocea was more closely related to T. squamosa than to T. maxima and they formed a monophyletic group. T. crocea and T. squamosa were sister taxa and sister group to T. maxima and T. gigas. Close affinity between T. crocea and T. squamosa was also supported by high similarity on nucleotide level (94.30%) and concordant with the results of the previous studies using mitochondrial 16S rRNA and nuclear 18S rRNA. Key words: phylogenetic relationships, Chametrachea, cytochrome c oxidase

Identifying Fishes through DNA Barcodes and Microarrays

Background: International fish trade reached an import value of 62.8 billion Euro in 2006, of which 44.6% are covered by the European Union. Species identification is a key problem throughout the life cycle of fishes: from eggs and larvae to adults in fisheries research and control, as well as processed fish products in consumer protection. Methodology/Principal Findings: This study aims to evaluate the applicability of the three mitochondrial genes 16S rRNA (16S), cytochrome b (cyt b), and cytochrome oxidase subunit I (COI) for the identification of 50 European marine fish species by combining techniques of ‘‘DNA barcoding’’ and microarrays. In a DNA barcoding approach, neighbour Joining (NJ) phylogenetic trees of 369 16S, 212 cyt b, and 447 COI sequences indicated that cyt b and COI are suitable for unambiguous identification, whereas 16S failed to discriminate closely related flatfish and gurnard species. In course of probe design for DNA microarray development, each of the markers yielded a high number of potentially species-specific probes in silico, although many of them were rejected based on microarray hybridisation experiments. None of the markers provided probes to discriminate the sibling flatfish and gurnard species. However, since 16S-probes were less negatively influenced by the ‘‘position of label’’ effect and showed the lowest rejection rate and the highest mean signal intensity, 16S is more suitable for DNA microarray probe design than cty b and COI. The large portion of rejected COI-probes after hybridisation experiments (.90%) renders the DNA barcoding marker as rather unsuitable for this high-throughput technology. Conclusions/Significance: Based on these data, a DNA microarray containing 64 functional oligonucleotide probes for the identification of 30 out of the 50 fish species investigated was developed. It represents the next step towards an automated and easy-to-handle method to identify fish, ichthyoplankton, and fish products

Predicting Startup Survival from Digital Traces: Towards a Procedure for Early Stage Investors

We investigate whether digital traces can be used to predict early stage startup survival. \ Based on common survival factors from the entrepreneurship literature, we mined the \ digital footprints of 542 entrepreneurs and their ventures. Using a context

Molecular Phylogeny of Giant Clams Based on Mitochondrial DNA Cytochrome C Oxidase I Gene

There is an uncertainty for the relationships among giant clam species of Tridacninae, in particular among species belongs to subgenus Chametrachea i.e. Tridacna crocea, T. maxima, and T. squamosa based on different genetic markers. This study examined the relationships among three species within subgenus Chametrachea compared to the previous studies. Neighbour Joining, Maximum Parsimony and Maximum Likelihood tree were constructed based on 455 bp of the mitochondrial DNA cytochrome c oxidase I gene from T. crocea, T. squamosa, T. maxima, T. gigas, and several sequences derived from Genbank for the outgroups. The results showed that giant clams formed a monophyletic group. Within Tridacna group, T. crocea was more closely related to T. squamosa than to T. maxima and they formed a monophyletic group. T. crocea and T. squamosa were sister taxa and sister group to T. maxima and T. gigas. Close affinity between T. crocea and T. squamosa was also supported by high similarity on nucleotide level (94.30%) and concordant with the results of the previous studies using mitochondrial 16S rRNA and nuclear 18S rRNA

A Twitter-based machine learning approach to measuring online legitimacy

Research indicates that interactions on social media can reveal remarkably valid predictions about future events. In this study, we show that online legitimacy as a measure of social appreciation based on Twitter content can be used to accurately predict new venture survival. Specifically, we analyze more than 187,000 tweets from 253 new ventures’ Twitter accounts using context-specific machine learning approaches. Our findings suggest that we can correctly discriminate failed ventures from surviving ventures in up to 76% of cases. With this study, we contribute to the ongoing discussion on the importance of building legitimacy online and provide an account of how to use machine learning methodologies in entrepreneurship research.peerReviewe

It’s a Peoples Game, Isn’t It?! A Comparison Between the Investment Returns of Business Angels and Machine Learning Algorithms

Investors increasingly use machine learning (ML) algorithms to support their early stage investment decisions. However, it remains unclear if algorithms can make better investment decisions and if so, why. Building on behavioral decision theory, our study compares the investment returns of an algorithm with those of 255 business angels (BAs) investing via an angel investment platform. We explore the influence of human biases and experience on BAs’ returns and find that investors only outperformed the algorithm when they had extensive investment experience and managed to suppress their cognitive biases. These results offer novel insights into the role of cognitive limitations, experience, and the use of algorithms in early stage investing.peerReviewe