In this issue: Biotechnology Journal 8/2009
Bibliographic record
Abstract
Abstract Biofuel production in the financial crisis Rabinovich , Biotechnol. J. 2009, 4, 1117–1123 Biotechnology by its nature goes through the cycles of booms and busts. This is particularly true for the biofuel area which depends on the prices of crude oil and biological source products, like corn, wheat, sugar and vegetable oil. The current financial crisis followed by the burst of the oil bubble has brought deep trouble to most biofuel manufacturers. It lead to cuts in venture funding and is triggering numerous bankruptcies, mergers and acquisitions. However, crude enzyme manufacturers which have developed stable fungal platforms for protein overexpression seem to be a safer alternative for investors. This article by Mikhail Rabinovich from Moscow, Russia, discusses several aspects of how white biotechnology, and especially the biofuel industry, is affected by the current financial situation. The biotechnology industry: What's next? Guertin , Biotechnol. J. 2009, 4, 1124–1131 The current financial crisis is not only affecting banks and car manufacturers worldwide. It also has a profound impact on other key areas of the economy including biotechnology. A new era for biotechnology that will be more and more influenced by financial, political, demographical and ecological issues has begun. Risk and money are most probably the two most important factors which rule biotechnology industries. Especially because developing drugs is certainly among the riskiest businesses as only one product out of 5000 potential candidates reaches the market. In addition to a short overview about the current crisis and challenges of the biotech industry, Pierre Guertin from Quebec, Canada, reviews recent analysts' reports. This article sheds light on what biotechnology has become and how it is heading towards new levels for health, prosperity and sustainability. Downstream processing in India Babu and Vijayalakshmi , Biotechnol. J. 2009, 4, 1132–1139 India holds a small share of 2% in the global biotech market, which is dominated by the healthcare sector. Moreover, India is a key player in bio‐generic pharmaceuticals. Exactly this area has been identified as one the new pathways of sustainability that will lead to paradigm‐shifting trends in biotech industries (Ernst&Young Global biotech report). Generics based on today's blockbusters should mitigate pricing pressures on innovative drugs and lead to better margins. The bottleneck in biotechnology is not anymore the production of recombinant proteins, but the purification of proteins as one of the most challenging tasks of downstream processing. This article highlights the major players of the Indian industrial, academic and governmental sectors in this area. Especially the potential of collaborations with European countries is pointed out.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.289 | 0.206 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".