A Literature Review on the Relationship between Disruption and Business Model Innovation: What Choices do Incumbents have?

In many industries, incumbents face or are aware of the threat of disruption because of ongoing digital innovation. Disruption literature, prior to the late ’90s’, alluded to incumbents’ failure or success for reasons such as the technology deployed by the organisation. However, a few years after the first publication of Christensen’s theory on disruption (1995), researchers, including Christensen, began to attribute the success or failure of organisations to business models and not to technology per se. Thus, how organisations innovate their business models explain how they will fare in the market. A systematic literature review of the extant literature on disruption and business models, between 1997 and 2019 was conducted. The content analysis revealed three key relationships between disruption and business model innovation: (1) Entrants deploying disruptive business models, (2) Incumbents creating new business models, and (3) Incumbents adapting existing business models

\u3cem\u3eIn silico\u3c/em\u3e Detection of EMS-Induced Mutations in an \u3cem\u3eArabis alpina\u3c/em\u3e Population

Arabis alpine (Alpine rock-cress weed) is a flowering plant, native to mountainous environments of thenorthern hemisphere. We analyzed 1,454,931,853 next-generation sequencing (NGS) reads from 38 sequenced Arabis alpine mutant individuals which that were mutagenized using the chemical mutagen, ethyl methanesulfphonate (EMS). Using the BWA short reads mapper, BWA, 95% (1,387,167,658) of the NGS reads mapped to Arabis alpine reference genome version 4. Using the SAMtools variant- detection algorithm, SAMtools, we detected a total of 1,457,917 mutations, with an average of 38,366 mutations per sample. Overall, the predicted mutations include 971,252 high-quality single nucleotide polymorphisms (SNPs) and 168,783 high-quality insertions and deletions (INDELs)

Analysis of Natural Variation in 30 Sorghum Landraces

Sorghum is a next generation of crop species for food grain, feedstock, beverage and biofuel production. To discover highly desirable agronomic traits in sorghum, we analyzed 3.42 billion DNA sequences derived from 30 sequenced sorghum landraces using next-generation sequencing (NGS) technology. Using the BWA short reads aligner, 97% of the sequenced reads mapped successfully to the sorghum reference genome. Using the SAMtools variant-calling algorithm, we detected 68.14 million mutations, including 61.32 million DNA base substitutions or single nucleotide polymorphisms (SNPs) and 6.81 million insertions and deletions (INDELs). In our preliminary analysis using the snpEff variant annotation tool, we predicted a total of 134,207 high-impact mutations and 1.81 million moderate-impact mutations in the 30 sequenced sorghum landraces

Use of Linear Discriminant Function Analysis in Five Yield Sub-Characters Relationship Study in 134 Cowpea (Vigna unguiculata (L.) Walp) Accessions

Variations in five yield sub-characters of cowpea in 134 accessions were studied. Data were collected on number of pods per plant, pod length, pod width, peduncle length and 100-seed weight. Differences among the accessions were significant based on four of the five characters, namely pod length, pod width, peduncle length and 100-seed weight. K-means cluster analysis grouped the 134 accessions into four distinct groups. Pairwise Mahalanobis 2 distance (D) among some of the groups was highly significant. From the study the yield sub-characters pod length, pod width, peduncle length and 100-seed weight contributed most to group separation in the cowpea accessions

Genome-wide profiling of uncapped mRNA

Gene transcripts are under extensive posttranscriptional regulation, including the regulation of their stability. A major route for mRNA degradation produces uncapped mRNAs, which can be generated by decapping enzymes, endonucleases, and small RNAs. Profiling uncapped mRNA molecules is important for the understanding of the transcriptome, whose composition is determined by a balance between mRNA synthesis and degradation. In this chapter, we describe a method to profile these uncapped mRNAs at the genome scale

Physicochemical and biological properties of different Cocoa Pod Husk-based composts

The objective was to evaluate the properties of cocoa pod husk-based composts for potential application as soil amendments for crop production. The physicochemical and biological properties of the compost types were analysed. Four compost types were prepared by mixing cocoa pod husk, poultry manure and Panicum maximum in different proportions. A phytotoxicity test was carried out using maize (Zea mays L.) to test whether the compost types contain substances that inhibit seed germination or growth of the radicle. Bulk densities of the compost types were higher than 0.160 Mg m-3, an indication that the compost types as soil amendment will restrict root growth thereby inhibiting plant growth. The average pH of the compost types falls within the optimum range of 6.5 to 8.5 and thus, the composts are stabilized. The compost types had high nitrogen content, so when utilized as a soil amendment would improve the nitrogen content of soils. Copper concentrations in the compost types were far below the WHO/FAO permissible limit of 100 mg kg-1, therefore can be applied at high rates without any problem of copper accumulation in soil. Phytophthora palmivora and Phytophthora megakarya were not detected from the compost types, therefore the compost types could be used without Phytophthora disease infection. Germination percentage and germination index showed that the analyzed compost types achieved high percentages of the germinating capacity of maize seeds and had no phytotoxic substances. The cocoa pod husk-based composts showed substantially varied physicochemical and biological properties suitable to support plant growth. The results clearly showed that, CPHcomp3 made from CPH residues, poultry manure and Panicum maximum at the ratio 6: 1: 2 mixture is recommended for use as a soil amendment for crop production

SeqTar: an effective method for identifying microRNA guided cleavage sites from degradome of polyadenylated transcripts in plants

In plants, microRNAs (miRNAs) regulate their mRNA targets by precisely guiding cleavages between the 10th and 11th nucleotides in the complementary regions. High-throughput sequencing-based methods, such as PARE or degradome profiling coupled with a computational analysis of the sequencing data, have recently been developed for identifying miRNA targets on a genome-wide scale. The existing algorithms limit the number of mismatches between a miRNA and its targets and strictly do not allow a mismatch or G:U Wobble pair at the position 10 or 11. However, evidences from recent studies suggest that cleavable targets with more mismatches exist indicating that a relaxed criterion can find additional miRNA targets. In order to identify targets including the ones with weak complementarities from degradome data, we developed a computational method called SeqTar that allows more mismatches and critically mismatch or G:U pair at the position 10 or 11. Precisely, two statistics were introduced in SeqTar, one to measure the alignment between miRNA and its target and the other to quantify the abundance of reads at the center of the miRNA complementary site. By applying SeqTar to publicly available degradome data sets from Arabidopsis and rice, we identified a substantial number of novel targets for conserved and non-conserved miRNAs in addition to the reported ones. Furthermore, using RLM 5′-RACE assay, we experimentally verified 12 of the novel miRNA targets (6 each in Arabidopsis and rice), of which some have more than 4 mismatches and have mismatches or G:U pairs at the position 10 or 11 in the miRNA complementary sites. Thus, SeqTar is an effective method for identifying miRNA targets in plants using degradome data sets

Cyber supply chain security: a cost benefit analysis using net present value

Cyber supply chain (CSC) security cost effectiveness should be the first and foremost decision to consider when integrating various networks in supplier inbound and outbound chains. CSC systems integrate different organizational network systems nodes such as SMEs and third-party vendors for business processes, information flows, and delivery channels. Adversaries are deploying various attacks such as RAT and Island-hopping attacks to penetrate, infiltrate, manipulate and change delivery channels. However, most businesses fail to invest adequately in security and do not consider analyzing the long term benefits of that to monitor and audit third party networks. Thus, making cost benefit analysis the most overriding factor. The paper explores the cost-benefit analysis of investing in cyber supply chain security to improve security. The contribution of the paper is threefold. First, we consider the various existing cybersecurity investments and the supply chain environment to determine their impact. Secondly, we use the NPV method to appraise the return on investment over a period of time. The approach considers other methods such as the Payback Period and Internal Rate of Return to analyze the investment appraisal decisions. Finally, we propose investment options that ensure CSC security performance investment appraisal, ROI, and business continuity. Our results show that NVP can be used for cost-benefit analysis and to appraise CSC system security to ensure business continuity planning and impact assessment

The database of experimentally supported targets: a functional update of TarBase

TarBase5.0 is a database which houses a manually curated collection of experimentally supported microRNA (miRNA) targets in several animal species of central scientific interest, plants and viruses. MiRNAs are small non-coding RNA molecules that exhibit an inhibitory effect on gene expression, interfering with the stability and translational efficiency of the targeted mature messenger RNAs. Even though several computational programs exist to predict miRNA targets, there is a need for a comprehensive collection and description of miRNA targets with experimental support. Here we introduce a substantially extended version of this resource. The current version includes more than 1300 experimentally supported targets. Each target site is described by the miRNA that binds it, the gene in which it occurs, the nature of the experiments that were conducted to test it, the sufficiency of the site to induce translational repression and/or cleavage, and the paper from which all these data were extracted. Additionally, the database is functionally linked to several other relevant and useful databases such as Ensembl, Hugo, UCSC and SwissProt. The TarBase5.0 database can be queried or downloaded from http://microrna.gr/tarbase

PAREsnip2: A tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules

Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cleavage and subsequent degradation of the mRNA. The resulting mRNA fragments, or degradome, provide evidence for these interactions, and thus degradome analysis has become an important tool for sRNA target prediction. Even so, with the continuing advances in sequencing technologies, not only are larger and more complex genomes being sequenced, but also degradome and associated datasets are growing both in number and read count. As a result, existing degradome analysis tools are unable to process the volume of data being produced without imposing huge resource and time requirements. Moreover, these tools use stringent, non-configurable targeting rules, which reduces their flexibility. Here, we present a new and user configurable software tool for degradome analysis, which employs a novel search algorithm and sequence encoding technique to reduce the search space during analysis. The tool significantly reduces the time and resources required to perform degradome analysis, in some cases providing more than two orders of magnitude speed-up over current methods