Background In some cases, a dentist cannot solve the difficulties a patient has with an implant because the implant system is unknown. Therefore, there is a need for a system for identifying the implant system of a patient from limited data that does not depend on the dentist’s knowledge and experience. The purpose of this study was to identify dental implant systems using a deep learning method. Methods A dataset of 1282 panoramic radiograph images with implants were used for deep learning. An object detection algorithm (Yolov3) was used to identify the six implant systems by three manufactures. To implement the algorithm, TensorFlow and Keras deep-learning libraries were used. After training was complete, the true positive (TP) ratio and average precision (AP) of each implant system as well as the mean AP (mAP), and mean intersection over union (mIoU) were calculated to evaluate the performance of the model. Results The number of each implant system varied from 240 to 1919. The TP ratio and AP of each implant system varied from 0.50 to 0.82 and from 0.51 to 0.85, respectively. The mAP and mIoU of this model were 0.71 and 0.72, respectively. Conclusions The results of this study suggest that implants can be identified from panoramic radiographic images using deep learning-based object detection. This identification system could help dentists as well as patients suffering from implant problems. However, more images of other implant systems will be necessary to increase the learning performance to apply this system in clinical practice.

Purpose: The purpose of this study was to develop a method for classifying dental arches using a convolutional neural network (CNN) as the first step in a system for designing removable partial dentures. Methods: Using 1184 images of dental arches (maxilla: 748 images; mandible: 436 images), arches were classified into four arch types: edentulous, intact dentition, arches with posterior tooth loss, and arches with bounded edentulous space. A CNN method to classify images was developed using Tensorflow and Keras deep learning libraries. After completion of the learning procedure, the diagnostic accuracy, precision, recall, F-measure and area under the curve (AUC) for each jaw were calculated for diagnostic performance of learning. The classification was also predicted using other images, and percentages of correct predictions (PCPs) were calculated. The PCPs were compared with the Kruskal-Wallis test (p = 0.05). Results: The diagnostic accuracy was 99.5% for the maxilla and 99.7% for the mandible. The precision, recall, and F-measure for both jaws were 0.25, 1.0 and 0.4, respectively. The AUC was 0.99 for the maxilla and 0.98 for the mandible. The PCPs of the classifications were more than 95% for all types of dental arch. There were no significant differences among the four types of dental arches in the mandible. Conclusions:The results of this study suggest that dental arches can be classified and predicted using a CNN. Future development of systems for designing removable partial dentures will be made possible using this and other AI technologies.

We measured primary production by phytoplankton in the south basin of Lake Baikal, Russia, by in situ 13 Cbicarbonate incubations within the period March-October in two consecutive years (1999 and 2000). Primary production was highest in the subsurface layer, possibly due to near-surface photoinhibition of photosynthesis, even under 0.8 m of ice cover in March. Areal primary production varied from 79 mg C m Ϫ2 day Ϫ1 (March) to 424 mg C m Ϫ2 day Ϫ1 (August), and annual primary production was roughly estimated as 75 g C m Ϫ2 year Ϫ1 , both of which are within the lower range of previous estimates. Size fractionation measurements revealed that phytoplankton in the Ͻ20 µm fraction accounted for 72%, 96%, and 85% of total primary production in March, August, and October, respectively. The contribution of picophytoplankton (Ͻ2 µm) to total primary production ranged from 41% to 62%. A large fraction (82%-98%) of particulate organic carbon was associated with particles in the Ͻ20 µm fraction. These results suggest that nano-and picophytoplankton play an important role as primary producers in the pelagic ecosystem of Lake Baikal.

Simple correlation and multiple regression analyses were performed to examine the relationship between primary productivity and environmental factors in the north basin of Lake Biwa. The primary production rates used in the analyses were estimated monthly or bimonthly during the growing season (April–November) in 1992, 1996 and 1997 with the 13C method. Elemental (C, N and P) contents of seston were used to assess nutrient conditions. Analyses revealed that 86% of variance in depth‐integrated primary production rates (areal PP) can be explained by changes in light intensity, and sestonic C, N and P concentrations. Water temperature had no effect on areal PP. To assess relative effects of light and nutrients on PP, the P:B ratio was estimated by normalizing PP with sestonic C. The areal P:B ratio correlated most significantly with the sestonic N:P ratio, followed by light intensity. When regression analyses were made at each depth, however, the P:B ratio correlated significantly only with the sestonic N:P ratio at 0 and 1 m depths, while light intensity was also incorporated into the regressions at deeper than 2.5 m. In these regressions, the P:B ratio was negatively correlated with sestonic N:P ratio but positively with light intensity. The results suggest that the primary production rate in this lake was mainly limited by P relative to N supply rates, but was not free from light limitation in a large part of the epilimnion. In Lake Biwa, the vertical water mixing regime as well as the nutrient supply seem to be important in determining the growth and composition of primary producers, since the surface mixing layer extends into 10–15 m depths during most of the growing season.

The purpose of this study is to develop a method for recognizing dental prostheses and restorations of teeth using a deep learning. A dataset of 1904 oral photographic images of dental arches (maxilla: 1084 images; mandible: 820 images) was used in the study. A deep-learning method to recognize the 11 types of dental prostheses and restorations was developed using TensorFlow and Keras deep learning libraries. After completion of the learning procedure, the average precision of each prosthesis, mean average precision, and mean intersection over union were used to evaluate learning performance. The average precision of each prosthesis varies from 0.59 to 0.93. The mean average precision and mean intersection over union of this system were 0.80 and 0.76, respectively. More than 80% of metallic dental prostheses were detected correctly, but only 60% of tooth-colored prostheses were detected. The results of this study suggest that dental prostheses and restorations that are metallic in color can be recognized and predicted with high accuracy using deep learning; however, those with tooth color are recognized with moderate accuracy.

The sibilant /s/ is produced by raising the tongue against the roof of the mouth to form a narrow constriction, which is adjusted so that the airstream emerging from it impinges on the incisors. However, the location where the sibilant sound occurs is unclear, as are the details of the mechanisms of its generation. In this study, we used a realistically shaped replica produced with a three-dimensional printer and demonstrated that turbulent flow was generated in the oral tract near the incisors and lips and that sufficiently developed turbulent flow generated a sound source up to 20,000 Hz at 333, 500, and 667 cm(3)/sec, which agrees with the range of physiological flow rates typical for /s/. The characteristics of the sound spectra agreed with those of the sibilant /s/ sound emitted by our control individual. Such a physical perspective could yield knowledge useful for oral surgery and speech science - for example, to predict how the generation of sibilants may be occasionally affected by orthodontic and prosthodontic treatments.

1. Some characteristics of the photosynthesis and primary production of benthic and planktonic algal communities were investigated in a littoral zone covered with gravel in the north basin of Lake Biwa, paying special attention to the recent development of filamentous green algae (FGA) in the benthic algal community. 2. Pmax (maximum gross photosynthesis rate) values of the benthic algal community (0.1–1.2 mg C mg chl. a−1 h−1) obtained from photosynthesis–irradiance (P–I) curves were lower than those of the planktonic algal community (2.4–11.5 mg C mg chl. a−1 h−1). This is apparently a result of the high degree of self shading in the benthic algal community and its low turnover as compared with that of the planktonic algal community. 3. Relatively high Ik values (150–200 μmol photon m−2 s−1) were observed in the benthic algal community only in June–July when a FGA, Spirogyra sp., was abundant. This reflected a photosynthetic characteristic of the Spirogyra itself, in which photosynthesis was saturated at high light intensity. 4. The FGA community established in the layer between planktonic and sessile (benthic algae except for FGA) algal communities. It brought about extraordinarily high organic matter production in the littoral zone at the expense of production in the sessile algal community.

Under conditions of low oxygen availability, the larvae of the stonefly Oyamia lugubris McLachlan demonstrate a ‘push‐up’ behavior that is thought to enhance respiratory efficiency. We conducted an experiment to investigate the effect of the oxygen supply on this behavior in winter and summer by using a lotic chamber and natural water. From the experiment, we determined the critical oxygen supply level below which the stonefly larvae are compelled to do push‐ups. There was a small difference in the critical oxygen supply level between the seasons. This result emphasizes that a novel measurement of the oxygen availability, that is, the oxygen supply, could be an important determinant of the distribution of aquatic insects.