An holistic view of UK military capability development

Through Life Capability Management (TLCM) is the dominant theme of proposed changes to UK defence acquisition, but progress has been hindered by a lack of agreed interpretations for key concepts. This paper provides some clarity for Capability, Network Enabled Capability (NEC), TLCM, and Affordability and notes, in particular, the fractal nature of capability. Through stakeholder analysis and concept maps, we identify some of the major challenges associated with TLCM. These include affordability (which is the motivation for TLCM but may also be its stumbling block); the increased priority of agility, adaptability, and flexibility in capability planning; and the need for appropriate TLCM metrics. The lack of an explicit learning mechanism within the capability planning process is also a major deficiency, because TLCM relies on effective knowledge management. The changing role of industry is considered and the need for an holistic view of capability is emphasised

The influence of the concept of capability-based management on the development of the systems engineering discipline

This paper explores the implications of a capability-based conceptual approach on the development of the systems engineering (SE) discipline. It deals with the identification of some potential limits and gaps of traditional SE approaches and demonstrates the need for new and innovative developments which support the concept of capability based engineering, especially as applied in the military domain and networking environments. The innovative approaches include partnership for capability planning and service descriptions for capability representations. The paper also presents a very brief assessment of the state-of-the-art of cognate domains such as capability based planning alongside requirements engineering and management, and considers the extent to which they address capability based concepts. The related concepts of system of systems (SoS) and the endeavour to extend SE to SoS are necessarily addressed

Site-Specific and High-Loading Immobilization of Proteins by Using Cohesin–Dockerin and CBM–Cellulose Interactions

Immobilization of enzymes enhances their properties for application in industrial processes as reusable and robust biocatalysts. Here, we developed a new immobilization method by mimicking the natural cellulosome system. A group of cohesin and carbohydrate-binding module (CBM)-containing scaffoldins were genetically engineered, and their length was controlled by cohesin number. To use green fluorescent protein (GFP) as an immobilization model, its C-terminus was fused with a dockerin domain. GFP was able to specifically bind to scaffoldin via cohesin–dockerin interaction, while the scaffoldin could attach to cellulose by CBM–cellulose interaction. Our results showed that this mild and convenient approach was able to achieve site-specific immobilization, and the maximum GFP loading capacity reached ∼0.508 μmol/g cellulose

Additional file 1 of ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization

Additional file 1: Figure S1. Dynamic PPI Networks. Figure S2. Comparison with Centrality Methods. Figure S3. AUC and RP curves of ECDEP compared on S. cerevisiae (BioGRID) dataset. Figure S4. AUC and RP curves of ECDEP compared on S. cerevisiae (Krogan) dataset. Figure S5. AUC and RP curves of ECDEP compared on M. musculus dataset. Figure S6. AUC and RP curves of ECDEP compared on C. elegans dataset. Figure S7. AUC and RP curves of ECDEP compared on S. cerevisiae (DIP) dataset. Figure S8. Comparison with machine learning and deep learning methods. Figure S9. Ablation study of features in ECDEP across six datasets evaluated with AUC score. Figure S10. Process of detaching each snapshot. Figure S11. Evaluate the results of detaching each snapshot with F1, AUC, and AP scores. Figure S12. Comparison of information from static network and dynamic network. Figure S13. Generate the intersection set of ECDEP and EP-EDL methods. Figure S14. Comparison of ECDEP with RNN-based methods. Figure S15. Compare ECDEP with canonical Graph Convolutional Network (GCN). Table S1. Version and sources of databases. Table S2. Download links of methods for comparison. Table S3. Process of essential proteins for different species. Table S4. Process of gene expression profiles. Table S5. PPI network details for different species and datasets. Table S6. Environment, package, and version requirements. Table S7. Hyperparameter settings of ECDEP model. Table S8. Experiment on different selections of M. musculus essential protein

FRAT1 knockdown suppresses plate colony formation and soft agar colony formation.

<p>(A, B) Equal numbers of parental U251, U251-neo, U251-NC and U251-S cells were seeded onto 60 mm dishes. After 14 days, the cells were fixed and stained with Giemsa (A). The average number of colonies formed in three independent experiments was quantified (B). (C, D) Equal numbers of U251, U251-neo, U251-NC and U251-S cells were plated in 0.3% soft agar and cultured for 14 days. Colony formation was photographed under the microscope (C) and scored (D).</p

RNAi-mediated knockdown of FRAT1 inhibits growth of U251 cells in vitro.

<p>Cell viability was measured using a MTT assay. Cell growth curves were determined by absorbance at 490 nm. *, P<0.01 for U251-S relative to each of the three control lines.</p

FRAT1 depletion decreases tumorigenicity in nude mice.

<p>(A) BALB/c-nu mice were injected subcutaneously with 1×10⁷ of U251, U251-neo, or U251-NC control cells; or U251-S FRAT1 knockdown cells. Representative tumor formation was photographed 40 days after injection. (B) Tumor sizes were determined by measuring the tumor volume every five days from 5 to 40 days after injection. (C) Average tumor weights of mice 40 days after injection are shown. Values represent means±SD obtained from three independent experiments. (D) Immunohistochemical analysis of FRAT1 expression in tumors in nude mice 40 days following injection.</p

RNAi-mediated knockdown of FRAT1 affects the cell cycle distribution of U251 cells in vitro.

<p><i>Left panel:</i> (A) parental U251, (B) U251-NC, (C) U251-neo and (D) U251-S were stained with propidium iodide. The DNA content and cell cycle were examined and analyzed by flow cytometry. <i>Right panel:</i> A histogram is provided showing the percentages of cells in each cell cycle phase as determined by gating of the flow cytometry.</p

RNA interference reduced the expression of FRAT1 in U251 cells.

<p>Down-regulation of FRAT1 mRNA and protein expression in U251-S cells as compared to the parental U251, U251-NC, and U251-neo control cell lines was confirmed by RT-PCR and Western blot (WB). GAPDH was amplified as an internal control for the RT-PCR, and β-actin levels were examined as a loading control for the Western blot. 1: parental U251 cells; 2: U251-NC; 3: U251-neo; 4: U251-S.</p

FRAT1 mRNA and protein levels in normal cultured primary astrocytes and SHG44, U87, U251 glioma cells as assessed by RT-PCR and Western blot analysis.

<p>For RT-PCR, specific FRAT1 primers yielded a 325-bp FRAT1 cDNA fragment, and internal control primers for GAPDH yielded a 402-bp GAPDH cDNA fragment. Western blot analysis (WB) of FRAT1 detected distinct bands with apparent molecular mass of 29 kDa. β-actin was assessed as a loading control. N: human normal astrocytes; 1: SHG44; 2: U87; 3: U251.</p