Modeling the formation of defensive gaps in basketball: Cutting on a teammate's drive.

Basketball is a game of simultaneous actions, and inter-player coordination is key for offensive success. One of the most challenging aspects in this regard is basket cutting on a teammate's drive. The ability to make these cuts is considered to be an artistic skill, mastered by only a handful of players. This skill is also hard to assess, as there is no method to measure the players' capability with respect to this quality-especially not automatically. Using SportVU data from the NBA, we created a mathematical model that identifies the openings in the defense which allow to perform a cut. Our model succeeds to generalize, as it detects these openings on average 139ms earlier than the actual cuts start and has an overall (balanced) accuracy of 0.818 on the test set. Having a tree-based gradient boosting classifier, we received a clear hierarchy of feature importance and were able to inspect the interactions between these attributes during action. This way, the model gives insights about the kind of defensive movements needed for a player to allow enough space to cut while in practical usage the analysis of the output can also help the coaching staff in designing play options and assessing player abilities. By paying more attention to the possible off ball movements during drives, offensive plays can become more versatile-benefiting the participants and the spectators alike.

Observability analysis of biochemical process models as a valuable tool for the development of mechanistic soft sensors.

By enabling the estimation of difficult-to-measure target variables using available indirect measurements, mechanistic soft sensors have become important tools for various bioprocess monitoring and control scenarios. Despite promising higher process efficiencies and increased process understanding, widespread application of soft sensors has been stalled by uncertainty about the feasibility and reliability of their estimations given present process analytical constraints. Observability analysis can provide an indication of the possibility and reliability of soft sensor estimations by analyzing the structural properties of first-principle (mechanistic) models. In addition, it can provide a criteria for selection of suitable measurement methods with respect to their information content; thereby leading to successful implementation of soft sensors in bioprocess development and manufacturing environments. We demonstrate the utility of observability analysis for two classes of upstream bioprocesses: the processes involving growth and ethanol formation by Saccharomyces cerevisiae and the process of penicillin production by Penicillium chrysogenum. Results obtained from laboratory-scale cultivations in addition to in-silico experiments enable a comparison of theoretical aspects of observability analysis and the real-life performance of soft sensors. By taking the expected error of measurements provided to the soft sensor into account, an innovative scaling approach facilitates a higher degree of comparability of observability results among various measurement configurations and process conditions.

Modulation of the amplitude of γ-band activity by stimulus phase enhances signal encoding.

Visual stimulation often leads to elevated fluctuations of the membrane potential in the γ-frequency range (25-70 Hz) in visual cortex neurons. Recently, we have found that the strength of γ-band fluctuations is coupled to the oscillation of the membrane potential at the temporal frequency of the stimulus, so that the γ-band fluctuations are stronger at depolarization peaks, but weaker at troughs of the stimulus frequency oscillation of the membrane potential. We hypothesized that this coupling may improve stimulus encoding. Here, we tested this hypothesis by using a single-compartment conductance-based neuron model, with parameters of the input adjusted to reproduce typical features of membrane potential and spike responses, recorded in cat visual cortical neurons in vivo during the presentation of moving gratings. We show that modulation of the γ-range membrane potential fluctuations by the amplitude of the slow membrane depolarization greatly improves stimulus encoding. Moreover, changing the degree of modulation of the γ-activity by the low-frequency signal within the range typically observed in visual cortex cells had a stronger effect on both the firing rates and information rates than changing the amplitude of the low-frequency stimulus itself. Thus, modulation of the γ-activity represents an efficient mechanism for regulation of neuronal firing and encoding of the temporal characteristics of visual stimuli.

Detection of pulses in a colored noise setting.

Cortical neurons are exposed to a considerable amount of synaptic background activity, which increases the neurons' conductance and which leads to a fluctuating membrane potential. Here we investigate how the presence and the properties of this background noise influence the ability of a neuron to detect transient inputs, a task that is important for coincidence detection as well as for the detection of synchronous spiking events in a neural system. Using a leaky integrate-and-fire neuron as well as a biologically more realistic Hodgkin-Huxley type point neuron we find that noise enhances the detection of subthreshold input pulses and that the phenomenon of stochastic resonance occurs. When the noise is colored, pulse detection becomes more robust, because the number of false positive events decreases with increasing temporal correlation while the number of correctly detected events is almost unaffected. Therefore, the optimal variance of the noise also changes with the degree of temporal correlations of the background activity. For the integrate-and-fire model these effects can be described using an ansatz by Brunel and Sergi [J. Theor. Biol. 195, 87 (1998)]. Numerical simulations show that the leaky integrate-and-fire model and the Hodgkin-Huxley type point neuron behave qualitatively similarly.

Optimal noise-aided signal transmission through populations of neurons.

Metabolic considerations and neurophysiological measurements indicate that biological neural systems prefer information transmission via many parallel low intensity channels, compared to few high intensity ones [S. B. Laughlin et al., Nature Neurosci. 1, 36 (1998)]. Furthermore, cortical neurons are exposed to a considerable amount of synaptic background activity, which increases the neurons' conductance and leads to a fluctuating membrane potential that, on average, is close to the threshold [A. Destexhe and D. Paré, J. Neurophysiol. 81, 1531 (1999)]. Recent studies have shown that noise can improve the transmission of subthreshold signals in populations of neurons, e.g., if their response is pooled. In general, the optimal noise level depends on the stimulus distribution and on the number of neurons in the population. In this contribution we show that for a large enough number of neurons the latter dependency becomes weak, such that the optimal noise level becomes almost independent of the number of neurons in the population. First we investigate a binary threshold model of neurons. We derive an analytic expression for the optimal noise level at each single neuron, which-for a large enough population size-depends only on quantities that are locally available to a single neuron. Using numerical simulations, we then verify the weak dependence of the optimal noise level on population size in a more realistic framework using leaky integrate-and-fire as well as Hodgkin-Huxley-type model neurons. Next we construct a cost function, where quality of information transmission is traded against its metabolic costs. Again we find that-for subthreshold signals-there is an optimal noise level which maximizes this cost. This noise level, however, is almost independent of the number of neurons, even for small population sizes, as numerical simulations using the Hodgkin-Huxley model show. Since the dependence of the optimal noise level on population size is weak for large enough populations, local neural adaptation is sufficient to adjust the level of noise to its optimal value.

A concept for the treatment of various dental bone defects.

Untreated dental bone defects usually lead to resorption of alveolar bone. Filling these defects with bone substitute material prevents resorption of bone, preserves the alveolar ridge, and provides sufficient bone for immediate or subsequent implant placement. A variety of bone substitutes is available. They differ in origin, consistency, particle size, porosity, and resorption characteristics. We have treated almost 1000 bony defect sites in 267 patients with the bone regeneration material Cerasorb. Being resorbed simultaneously with the formation of new bone, it is completely replaced by the patient's own vital bone within 6 to 12 months. The representative cases described in this paper demonstrate the successful use of the pure-phase beta-tricalcium phosphate ceramic in the treatment of all dental bone defects.