Novel neuropeptides of G-protein coupled receptor (GPCR) ligands are shown to localize in brain and perform a range of physiological functions including feeding regulation. Here, we will describe the distribution and localization of these neuropeptides identified very recently and to examine their involvement in neuronal networks, particularly feeding regulation. This review concerns some novel GPCR ligands of feeding-regulation factors such as orexin, ghrelin, galanin-like peptide (GALP) and neuropeptide W (NPW), such as those described by our research group and others, and neuronal interactions among these neuropeptides in the hypothalamus. Cross-talk among several these neuropeptides-containing neuron types in the hypothalamus plays a role in determining feeding states. We show structural and functional characteristics of novel neuropeptides and summarize the known interactions between several these neuron types and leptin-targeting neurons in the hypothalamus. Research in this field will serve an important role of clarifying neurologically-based causes for appetite dysfunctions and energy homeostasis in establishing therapies for people suffering such conditions.

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In order to design functional occlusal surfaces, dental CAD systems have to simulate tooth articulation during excursions. In commercial dental CAD systems, however, a method for the simulation of patient's tooth excursions has not yet been established. We have developed a tooth excursion simulator for dental CAD by numerical simulating a dental articulator. Tooth excursion paths in the simulator are decided on adjustable components such as sagittal inclination of the condylar housing denoted as C_s; and sagittal inclination of the incisal table, denoted as I_s. Our previous tooth excursion simulator, however, was not easily customizable to be applied to specific patients. The purpose of this study is to develop an easily adjustable function of the virtual articulator in accordance with patient's tooth excursions.
In order to optimize the adjustable parameters of the simulator in accordance with patient's tooth excursions, we employed the following procedure: 1) we took a functional occlusal impression formed by a tooth excursion, 2) we computed the value of parameters C_s and I_s to make a simulated virtual impression agree with the actual occlusal impression, and 3) we adjusted the virtual articulator. Then, we defined an evaluation parameter represented as the “agreement,” and proposed an advanced 3D-registration-based method to automatically adjust the articulator in accordance with patient's functional occlusal impression.
Experiments were carried out, as follows: 1) we created a functional occlusal impression using a real articulator, 2) we adjusted the virtual articulator from the impression by means of the proposed method, and 3) we evaluated the values of the articulator parameters. To optimize articulator parameters C_s and I_s for patient's impression, the simulated annealing method was employed with an evaluation parameter. Empirical results are summarized as follows: 1) the evaluation value was converged into the maximum value by the SA, 2) the estimated value included error components, ΔI_s = 0.9° and = ΔC_s = 0.6°, and 3) a local maximum was observed near the optimum solution. From these results, the proposed method was verified to be able to adjust the simulator with the accuracy required for its clinical application.

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Due to difficulties in simultaneous electromyographic (EMG) recording of all the jaw muscles which are involved in chewing, direct evidences of the reduction of controlled degree of freedom (DOF) of chewing, including the information on jaw muscle synergy for this motor task, are scarce. This study aimed to obtain indirect evidence for the reduction of controlled DOF of chewing, by testing the predictability of chewing trajectories from the EMG activities of selected jaw muscles. Because this prediction is possible only when other jaw muscles consistently change their activities simultaneously with the selected muscles, a successful prediction will provide an indirect evidence of synergy, although the inverse may not be true. Methods: The lower incisal path and the EMGs of bilateral masseter, anterior temporalis, and submandibular muscles were recorded during unilateral gum chewing in 4 healthy volunteers. For each of the 200 chewing cycles, the durations of opening, closing, and occluding phases were divided into 10 equal time intervals so as to produce a total of 31 time points. Initially, the incisal path and EMGs in each cycle were described using the 3D coordinates and the rectified EMG amplitudes at the indicated time points, respectively, and then reduced into the data sets comprising the principal component (PC) scores, which account for ≥ 90% of the variations, by a PC analysis. Finally, an artificial neural network (ANN) model, which associates EMGs to incisal path data sets, was trained using 50% of the data sets (learning data) by using a back-propagation learning algorithm, and inspected using the remaining 50% (inspecting data). Results: The ANN model performed best after some training. The incisal paths for the learning and inspecting data sets were predicted successfully. The mean error among the 31 points was 0.7 and 0.8 mm for the learning and inspecting data sets, respectively. The largest errors, 1.5 and 1.8 mm, respectively, were observed during the later opening phase. Conclusion: These findings may provide an indirect evidence of the reduction of controlled DOF in chewing.

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It has been reported that the tapping movement of the jaw is centrally programmed, as is chewing. However, the functional involvement of the neck muscles in the coordination of jaw and head movements has not been fully clarified. The present study carried out simultaneous recordings of surface electromyography (EMG) of the jaw and neck muscles along with three-dimensional jaw and head movements to ascertain how these muscles were coordinated in their functions.
Eleven healthy male subjects had EMGs recorded from their right masseter and suprahyoid muscles and both sides of their sternocleidomastoid (SCM) and posterior cervical (PC) muscles. First, the subject was instructed to retract his jaw as much as possible from an intercuspal position to the so-called central relation (CR) to compare EMG activities between the positions. Second, the subject was instructed to jaw tap at 75/60 Hz and chew gum on the right side for 12 seconds.
All EMG activities increased in the CR. The amplitudes of jaw and head movements were larger in the tapping motion than in the chewing one while the coefficient variations of these values were less in tapping than in chewing. Although the amplitude of the jaw movement tended to be related to the head movement, this relationship was not linear among the subjects. SCM and PC muscles were active in the jaw opening phase. The SCM muscle showed unique activity in the jaw closing phase of chewing; this was significant on the working side.
The present results strongly suggested that the SCM and PC muscles are differentially involved in the coordination of jaw and head movements.

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