Philosophy and Science in Leibniz

This paper explores the question of Leibniz’s contribution to the rise of modern ‘science’. To be sure, it is now generally agreed that the modern category of ‘science’ did not exist in the early modern period. At the same time, this period witnessed a very important stage in the process from which modern science eventually emerged. My discussion will be aimed at uncovering the new enterprise, and the new distinctions which were taking shape in the early modern period under the banner of the old Aristotelian terminology. I will argue that Leibniz begins to theorize a distinction between physics and metaphysics that tracks our distinction between the autonomous enterprise of science in its modern meaning, and the enterprise of philosophy. I will try to show that, for Leibniz, physics proper is the study of natural phenomena in mathematical and mechanical terms without recourse for its explanations to metaphysical notions. This autonomy, however, does not imply for Leibniz that physics can say on its own all that there is to be said about the natural world. Quite the opposite. Leibniz inherits from the Aristotelian tradition the view that physics needs metaphysical roots or a metaphysical grounding. For Leibniz, what is ultimately real is reached by metaphysics, not by physics. This is, in my view, Leibniz’s chief insight: the new mathematical physics is an autonomous enterprise which offers its own kind of explanations but does not exhaust what can (and should) be said about the natural world

Comparison study on k-word statistical measures for protein: From sequence to 'sequence space'

<p>Abstract</p> <p>Background</p> <p>Many proposed statistical measures can efficiently compare protein sequence to further infer protein structure, function and evolutionary information. They share the same idea of using <it>k</it>-word frequencies of protein sequences. Given a protein sequence, the information on its related protein sequences hasn't been used for protein sequence comparison until now. This paper proposed a scheme to construct protein 'sequence space' which was associated with protein sequences related to the given protein, and the performances of statistical measures were compared when they explored the information on protein 'sequence space' or not. This paper also presented two statistical measures for protein: <it>gre.k </it>(generalized relative entropy) and <it>gsm.k </it>(gapped similarity measure).</p> <p>Results</p> <p>We tested statistical measures based on protein 'sequence space' or not with three data sets. This not only offers the systematic and quantitative experimental assessment of these statistical measures, but also naturally complements the available comparison of statistical measures based on protein sequence. Moreover, we compared our statistical measures with alignment-based measures and the existing statistical measures. The experiments were grouped into two sets. The first one, performed via ROC (Receiver Operating Curve) analysis, aims at assessing the intrinsic ability of the statistical measures to discriminate and classify protein sequences. The second set of the experiments aims at assessing how well our measure does in phylogenetic analysis. Based on the experiments, several conclusions can be drawn and, from them, novel valuable guidelines for the use of protein 'sequence space' and statistical measures were obtained.</p> <p>Conclusion</p> <p>Alignment-based measures have a clear advantage when the data is high redundant. The more efficient statistical measure is the novel <it>gsm.k </it>introduced by this article, the <it>cos.k </it>followed. When the data becomes less redundant, <it>gre.k </it>proposed by us achieves a better performance, but all the other measures perform poorly on classification tasks. Almost all the statistical measures achieve improvement by exploring the information on 'sequence space' as word's length increases, especially for less redundant data. The reasonable results of phylogenetic analysis confirm that <it>Gdis.k </it>based on 'sequence space' is a reliable measure for phylogenetic analysis. In summary, our quantitative analysis verifies that exploring the information on 'sequence space' is a promising way to improve the abilities of statistical measures for protein comparison.</p

The Cyst-Dividing Bacterium Ramlibacter tataouinensis TTB310 Genome Reveals a Well-Stocked Toolbox for Adaptation to a Desert Environment

Ramlibacter tataouinensis TTB310T (strain TTB310), a betaproteobacterium isolated from a semi-arid region of South Tunisia (Tataouine), is characterized by the presence of both spherical and rod-shaped cells in pure culture. Cell division of strain TTB310 occurs by the binary fission of spherical “cyst-like” cells (“cyst-cyst” division). The rod-shaped cells formed at the periphery of a colony (consisting mainly of cysts) are highly motile and colonize a new environment, where they form a new colony by reversion to cyst-like cells. This unique cell cycle of strain TTB310, with desiccation tolerant cyst-like cells capable of division and desiccation sensitive motile rods capable of dissemination, appears to be a novel adaptation for life in a hot and dry desert environment. In order to gain insights into strain TTB310's underlying genetic repertoire and possible mechanisms responsible for its unusual lifestyle, the genome of strain TTB310 was completely sequenced and subsequently annotated. The complete genome consists of a single circular chromosome of 4,070,194 bp with an average G+C content of 70.0%, the highest among the Betaproteobacteria sequenced to date, with total of 3,899 predicted coding sequences covering 92% of the genome. We found that strain TTB310 has developed a highly complex network of two-component systems, which may utilize responses to light and perhaps a rudimentary circadian hourglass to anticipate water availability at the dew time in the middle/end of the desert winter nights and thus direct the growth window to cyclic water availability times. Other interesting features of the strain TTB310 genome that appear to be important for desiccation tolerance, including intermediary metabolism compounds such as trehalose or polyhydroxyalkanoate, and signal transduction pathways, are presented and discussed

A frameshift error detection algorithm for DNA sequencing projects.

During the determination of DNA sequences, frameshift errors are not the most frequent but they are the most bothersome as they corrupt the amino acid sequence over several residues. Detection of such errors by sequence alignment is only possible when related sequences are found in the databases. To avoid this limitation, we have developed a new tool based on the distribution of non-overlapping 3-tuples or 6-tuples in the three frames of an ORF. The method relies upon the result of a correspondence analysis. It has been extensively tested on Bacillus subtilis and Saccharomyces cerevisiae sequences and has also been examined with human sequences. The results indicate that it can detect frameshift errors affecting as few as 20 bp with a low rate of false positives (no more than 1.0/1000 bp scanned). The proposed algorithm can be used to scan a large collection of data, but it is mainly intended for laboratory practice as a tool for checking the quality of the sequences produced during a sequencing project

Bacterial transformation: distribution, shared mechanisms and divergent control.

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