Calorie restriction alters the mechanisms of radiation-induced mouse thymic lymphomagenesis.

Calorie restriction (CR) suppresses not only spontaneous but also chemical- and radiation-induced carcinogenesis. Our previous study revealed that the cancer-preventive effect of CR is tissue dependent and that CR does not effectively prevent the development of thymic lymphoma (TL). We investigated the association between CR and the genomic alterations of resulting TLs to clarify the underlying resistance mechanism. TLs were obtained from previous and new experiments, in which B6C3F1 mice were exposed to radiation at 1 week of age and fed with a CR or standard (non-CR) diet from 7 weeks throughout their lifetimes. All available TLs were used for analysis of genomic DNA. In contrast to the TLs of the non-CR group, those of the CR group displayed suppression of copy-neutral loss of heterozygosity (LOH) involving relevant tumor suppressor genes (Cdkn2a, Ikzf1, Trp53, Pten), an event regarded as cell division-associated. However, CR did not affect interstitial deletions of those genes, which were observed in both groups. In addition, CR affected the mechanism of Ikzf1 inactivation in TLs: the non-CR group exhibited copy-neutral LOH with duplicated inactive alleles, whereas the CR group showed expression of dominant-negative isoforms accompanying a point mutation or an intragenic deletion. These results suggest that, even though CR reduces cell division-related genomic rearrangements by suppressing cell proliferation, tumors arise via diverse carcinogenic pathways including inactivation of tumor suppressors via interstitial deletions and other mutations. These findings provide a molecular basis for improved prevention strategies that overcome the CR resistance of lymphomagenesis.

Automatic Detection of Chewing and Swallowing.

A series of eating behaviors, including chewing and swallowing, is considered to be crucial to the maintenance of good health. However, most such behaviors occur within the human body, and highly invasive methods such as X-rays and fiberscopes must be utilized to collect accurate behavioral data. A simpler method of measurement is needed in healthcare and medical fields; hence, the present study concerns the development of a method to automatically recognize a series of eating behaviors from the sounds produced during eating. The automatic detection of left chewing, right chewing, front biting, and swallowing was tested through the deployment of the hybrid CTC/attention model, which uses sound recorded through 2ch microphones under the ear and weak labeled data as training data to detect the balance of chewing and swallowing. N-gram based data augmentation was first performed using weak labeled data to generate many weak labeled eating sounds to augment the training data. The detection performance was improved through the use of the hybrid CTC/attention model, which can learn the context. In addition, the study confirmed a similar detection performance for open and closed foods.

Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning.

The demand for mobile data communication has been increasing owing to the diversification of its purposes and the increase in the number of mobile devices accessing mobile networks. Users are experiencing a degradation in communication quality due to mobile network congestion. Therefore, improving the bandwidth utilization efficiency of cellular infrastructure is crucial. We previously proposed a mobile data offloading protocol (MDOP) for improving the bandwidth utilization efficiency. Although this method balances a load of evolved node B by taking into consideration the content delay tolerance, accurately balancing the load is challenging. In this paper, we apply deep reinforcement learning to MDOP to solve the temporal locality of a traffic. Moreover, we examine and evaluate the concrete processing while considering a delay tolerance. A comparison of the proposed method and bandwidth utilization efficiency of MDOP showed that the proposed method reduced the network traffic in excess of the control target value by 35% as compared with the MDOP. Furthermore, the proposed method improved the data transmission ratio by the delay tolerance range. Consequently, the proposed method improved the bandwidth utilization efficiency by learning how to provide the bandwidth to the user equipment when MDOP cannot be used to appropriately balance a load.