Abstract: The invention herein relates generally to enhancing the survival and activity of probiotic Lactobacillus reuteri strains in mammals by pre-conditioning L. reuteri with GOS during cultivation and to a surprisingly high symbiotic effect of such a pre-conditioned probiotic L. reuteri strain when administered together with GOS further added in the composition. The invention therefore comprises methods for cultivating and manufacturing probiotic L. reuteri strains, and preparing products containing such pre-conditioned strains in combination with additional amounts of GOS. In more detail the present invention relates to a probiotic composition comprising a probiotic L. reuteri strain, obtained by a step of growing the bacteria in a medium comprising GOS, and administering the pre-conditioned bacteria together with further GOS in the final product. The inventors have found that such method induces surprisingly high and unexpected symbiotic beneficial effects of the probiotic bacteria in the gastrointestinal tract.

Abstract: The present invention relates to a probiotic Lactobacillus reuteri strain obtained by growing the bacteria in a medium comprising galacto-oligosaccharides (GOS), so called pre-conditioning. The pre-conditioning method provides boosting effects of the probiotic L. reuteri strain, such as improved mineral absorption, in the gastrointestinal tract. The invention comprises methods for manufacturing and cultivating probiotic Lactobacillus reuteri strains, and products and first and second medical uses of such strains.

Abstract: The invention relates to the use of composition comprising sialylated and fucosylated human milk oligosaccharides (HMOs) for enhancing bone development and/or bone strength, preferably a nutritional composition for infants or young children.

Abstract: The present invention relates to biological markers of metabolic bone diseases or disorders such as osteoporosis and related methods using thereof. The invention further relates to the use of those biomarkers and related material and assays in the diagnosis of subjects at risk of developing such disorders or monitoring the effects of a treatment thereof.

Abstract: The present invention relates to biological markers of metabolic bone diseases or disorders such as osteoporosis and related methods using thereof. The invention further relates to the use of those biomarkers and related material and assays in the diagnosis of subjects at risk of developing such disorders or monitoring the effects of a treatment thereof.

Abstract: The invention relates to an implantable pacemaker. This pacemaker comprises at least one conducting cable (11) electrically connecting a control box (10) to at least one electrode placed at a point in the patient's heart, the said control box (10) comprising a power supply system and an electronic system, and is characterised in that at least part of the said electronic system is remote from the control box (10) and is located at the at least one electrode to form an electrode module (13, 14a, 14b), the electrode module being placed on the outside surface of the heart.

Abstract: Disclosed are methods and apparatus for reporting significant data mining changes. In general, embodiments of the present invention address the shortcomings of the prior art through comparison over time of prediction model characteristics, such as inferences. Embodiments of the present invention detect trends in the model itself by detecting changes in levels of correlation (or any other model aspect) between individual elements of input data and targets of predictions. In this specific embodiment, users of the model are preferably alerted when an input characteristic or other model aspect, which was not important before, becomes important and when an input characteristic, which was important, loses its importance.

Abstract: Disclosed are methods and apparatus for optimizing results produced by a predictive model in order to determine which action to perform out of a plurality of actions. In an operation (a), a plurality of goal metrics are provided for a plurality of possible actions based on a plurality of input conditions. One or more of the goal metrics are produced by one or more predictive models. In an operation (b), the plurality of goal metrics are normalized. In an operation (c), for each possible action a total of each of the normalized goal metrics multiplied by a corresponding predetermined weight is determined. In an operation (d), the totals determined for the plurality of possible actions are compared to thereby determine a highest total. In an operation (e), an action selected from the plurality of possible actions is performed, where the selected action has the highest total.

Abstract: Disclosed are methods and apparatus for evaluating a certainty characteristic of a predictive model. When a decision needs to be implemented, the predictive model is utilized unless the certainty characteristic of such model indicates that the predictive model results are unacceptably uncertain and should not be used. Otherwise, the predictive model is used to reach a decision. In a further embodiment, randomization is introduced into the results of the predictive model (when utilized for a decision). The amount of randomization is tied to the amount of uncertainty of results of the model to thereby balance exploitation and exploration goals.

Abstract: Disclosed are methods and apparatus for predicting an expected value associated with an end event of an event sequence. In one embodiment, the following operations are performed: (a) providing a current set of input attributes and contextual data collected during performance of previous event sequences; (b) predicting a plurality of expected values for going from a first event of a known event sequence to each of a plurality of subsequent events of the known event sequence based at least on the current set of input attributes and the collected contextual data; and (c) predicting an expected value for going from a first event of an unknown event sequence to an end event of such unknown event sequence based on at least two of the expected values predicted for the known event sequence and based at least on the current set of input attributes and the collected contextual data.