Neural Structures within Human Meniscofemoral Ligaments: A Cadaveric Study.

Aim. To investigate the existence of neural structures within the meniscofemoral ligaments (MFLs) of the human knee. Methods. The MFLs from 8 human cadaveric knees were harvested. 5 μm sections were H&E-stained and examined under light microscopy. The harvested ligaments were then stained using an S100 monoclonal antibody utilising the ABC technique to detect neural components. Further examination was performed on 60–80 nm sections under electron microscopy. Results. Of the 8 knees, 6 were suitable for examination. From these both MFLs existed in 3, only anterior MFLs were present in 2, and an isolated posterior MFL existed in 1. Out of the 9 MFLs, 4 demonstrated neural structures on light and electron microscopy and this was confirmed with S100 staining. The ultrastructure of these neural components was morphologically similar to mechanoreceptors. Conclusion. Neural structures are present in MFLs near to their meniscal attachments. It is likely that the meniscofemoral ligaments contribute not only as passive secondary restraints to posterior draw but more importantly to proprioception and may therefore play an active role in providing a neurosensory feedback loop. This may be particularly important when the primary restraint has reduced function as in the posterior cruciate ligament—deficient human knee

Approaches to measuring inconsistent information

Abstract. Measures of quantity of information have been studied extensively for more than fifty years. The seminal work on information theory is by Shannon [67]. This work, based on probability theory, can be used in a logical setting when the worlds are the possible events. This work is also the basis of Lozinskii’s work [48] for defining the quantity of information of a formula (or knowledgebase) in propositional logic. But this definition is not suitable when the knowledgebase is inconsistent. In this case, it has no classical model, so we have no “event ” to count. This is a shortcoming since in practical applications (e.g. databases) it often happens that the knowledgebase is not consistent. And it is definitely not true that all inconsistent knowledgebases contain the same (null) amount of information, as given by the “classical information theory”. As explored for several years in the paraconsistent logic community, two inconsistent knowledgebases can lead to very different conclusions, showing that they do not convey the same information. There has been som