ObjectiveThis study investigated payments made by pharmaceutical companies to oncology specialists in Japan, what the payments were for and whether the receipt of such payments contravened any conflict of interest (COI) regulations.Design, setting and participantsPayment data to physicians, as reported by all pharmaceutical companies belonging to the Japan Pharmaceutical Manufacturers Association, were retrospectively extracted for 2016. Of the named individual recipients of payments, all certified oncologists were identified, using certification data from the Japanese Society of Medical Oncology (JSMO). The individual specialisations of each of the oncologists was also identified.OutcomePayments to individual cancer specialists and what they were for were identified. Factors associated with receipt of higher value payments and payment flows to specialties were determined. Companies selling oncology drugs with annual sales of ≥5 billion yen (£33.9 million, €40.2 million and $46.0 million) (high revenue-generating drugs) were identified.ResultsIn total, 59 companies made at least one payment to oncologists. Of the 1080 oncology specialists identified, 763 (70.6%) received at least one payment, while 317 received no payment. Of the 763, some 142 (13.1%) receiving at least 1 million yen (£6,800, €8,000 and $9200) accounted for 71.5% of the total. After adjustment of covariates, working for university hospitals and cancer hospitals and male gender were key factors associated with larger monetary payments. Payments preferentially targeted on cancer specialties using high revenue-generating drugs. The JSMO has its own COI policy for its members, but the policy did not mention any specific guidelines for certified oncology specialists.ConclusionFinancial relationships were identified and quantified between pharmaceutical companies and oncology specialists, but the extent and worth varied significantly. Given the frequency and amounts of money involved in such linkages, it would be beneficial for specific COI regulations to be developed and policed for oncologists.

Scientific communication through social media, particularly Twitter has been gaining importance in recent years. As such, it is critical to understand how information is transmitted and dispersed through outlets such as Twitter, particularly in emergency situations where there is an urgent need to relay scientific information. The purpose of this study is to examine how original tweets and retweets on Twitter were used to diffuse radiation related information after the Fukushima Daiichi nuclear power plant accident. Out of the Twitter database, we purchased all tweets (including replies) and retweets related to Fukushima Daiichi nuclear power plant accident and or radiation sent from March 2nd, 2011 to September 15th, 2011. This time frame represents the first six months after the East Japan earthquake, which occurred on March 11th, 2011. Using the obtained data, we examined the number of tweets and retweets and found that only a small number of Twitter users were the source of the original posts that were retweeted during the study period. We have termed these specific accounts as “influencers”. We identified the top 100 influencers and classified the contents of their tweets into 3 groups by analyzing the document vectors of the text. Then, we examined the number of retweets for each of the 3 groups of influencers, and created a retweet network diagram to assess how the contents of their tweets were being spread. The keyword “radiation” was mentioned in over 24 million tweets and retweets during the study period. Retweets accounted for roughly half (49.7%) of this number, and the top 2% of Twitter accounts defined as “influencers” were the source of the original posts that accounted for 80.3% of the total retweets. The majority of the top 100 influencers had individual Twitter accounts bearing real names. While retweets were intensively diffused within a fixed population, especially within the same groups with similar document vectors, a group of influencers accounted for the majority of retweets one month after the disaster, and the share of each group did not change even after proven scientific information became more available.

In this study, a stationary front is automatically detected from weather data using a U-Net deep convolutional neural network. The U-Net trained the transformation process from single/multiple physical quantities of weather data to detect stationary fronts using a 10-year data set. As a result of applying the trained U-Net to a 1-year untrained data set, the proposed approach succeeded in detecting the approximate shape of seasonal fronts with the exception of typhoons. In addition, the wind velocity (zonal and meridional components), wind direction, horizontal temperature gradient at 1000 hPa, relative humidity at 925 hPa, and water vapor at 850 hPa yielded high detection performance. Because the shape of the front extracted from each physical quantity is occasionally different, it is important to comprehensively analyze the results to make a final determination.

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