Multimedia information sharing in the heterogeneous environment

Simultaneous polydrug use (SPU) may represent a greater incremental risk factor for human health than concurrent polydrug use (CPU). However, few studies have examined these patterns of use in relation to health issues, particularly with regard to the number of drugs used. In the present study, we have analyzed data from a representative sample of 5734 young Swiss males from the Cohort Study on Substance Use Risk Factors. Exposure to drugs (i.e., alcohol, tobacco, cannabis, and 15 other illicit drugs), as well as mental, social and physical factors, were studied through regression analysis. We found that individuals engaging in CPU and SPU followed the known stages of drug use, involving initial experiences with licit drugs (e.g., alcohol and tobacco), followed by use of cannabis and then other illicit drugs. In this regard, two classes of illicit drugs were identified, including first uppers, hallucinogens and sniffed drugs; and then "harder" drugs (ketamine, heroin, and crystal meth), which were only consumed by polydrug users who were already taking numerous drugs. Moreover, we observed an association between the number of drugs used simultaneously and social issues (i.e., social consequences and aggressiveness). In fact, the more often the participants simultaneously used substances, the more likely they were to experience social problems. In contrast, we did not find any relationship between SPU and depression, anxiety, health consequences, or health. We identified some associations with SPU that were independent of CPU. Moreover, we found that the number of concurrently used drugs can be a strong factor associated with mental and physical health, although their simultaneous use may not significantly contribute to this association. Finally, the negative effects related to the use of one substance might be counteracted by the use of an additional substance.

ABSTRACTBackground: Alcohol and marijuana are the most commonly used substances among adolescents but little is known about patterns of co-use. Objectives: This study examined patterns of concurrent (not overlapping) and simultaneous (overlapping) use of alcohol and marijuana among adolescents. Methods: Data from US-national samples of 12th graders (N = 84,805, 48.4% female) who participated in the Monitoring the Future study from 1976 to 2016 and who used alcohol and/or marijuana in the past 12 months were used to identify latent classes of alcohol use, marijuana use, and simultaneous alcohol and marijuana (SAM) use. Results: A four-class solution indicated four patterns of use among adolescents: (1) Simultaneous alcohol and marijuana (SAM) use with binge drinking and recent marijuana use (SAM-Heavier Use; 11.2%); (2) SAM use without binge drinking and with recent marijuana use (SAM-Lighter Use; 21.6%); (3) Marijuana use and alcohol use but no SAM use (Concurrent Use; 10.7%); and (4) Alcohol use but no marijuana or SAM use (Alcohol-Only Use; 56.4%). Membership in either SAM use class was associated with a higher likelihood of truancy, evenings out, and use of illicit drugs other than marijuana. SAM-Heavier Use, compared to SAM-Lighter Use, class members were more likely to report these behaviors and be male, and less likely to have college plans. Conclusions: Among 12th graders who use both alcohol and marijuana, the majority use simultaneously, although not all use heavily. Given the recognized increased public health risks associated with simultaneous use, adolescent prevention programming should include focus on particular risks of simultaneous use.

This article examines noncausal associations between high school seniors' alcohol and marijuana use status and rates of self-reported unsafe driving in the past 12 months. Analyses used data from 72,053 students collected through annual surveys of nationally representative cross-sectional samples of U.S. 12th-grade students from 1976 to 2011. Two aspects of past-12-month alcohol and marijuana use were examined: (a) use frequency and (b) status as a nonuser, single substance user, concurrent user, or simultaneous user. Measures of past-12-month unsafe driving included any tickets/warnings or accidents, as well as tickets/warnings or accidents following alcohol or marijuana use. Analyses explored whether an individual's substance use frequency and simultaneous use status had differential associations with their rate of unsafe driving. Higher substance use frequency (primarily alcohol use frequency) was significantly and positively associated with unsafe driving. The rate of engaging in any unsafe driving was also significantly and positively associated with simultaneous use status, with the highest rate associated with simultaneous use, followed by concurrent use, followed by use of alcohol alone. Individuals who reported simultaneous use most or every time they used marijuana had the highest likelihood of reporting unsafe driving following either alcohol or marijuana use. This article expands the knowledge on individual risk factors associated with unsafe driving among teens. Efforts to educate U.S. high school students (especially substance users), parents, and individuals involved in prevention programming and driver's education about the increased risks associated with various forms of drug use status may be useful.

The main problem: The article deals with the issues of hiding text information in a graphic file. A formula for hiding text information in image pixels is proposed. A steganography scheme for embedding secret text in random image pixels has been developed. Random bytes are pre-embedded in each row of pixels in the source image. As a result of the operations performed, a key image is obtained. The text codes are embedded in random bytes of pixels of a given RGB channel. To form a secret message, the characters of the ASCII code table are used. Demo encryption and decryption programs have been developed in the Python 3.5.2 programming language. A graphic file is used as the decryption key. Purpose: To develop an algorithm for embedding text information in random pixels of an image. Methods: Among the methods of hiding information in graphic images, the LSB method of hiding information is widely used, in which the lower bits in the image bytes responsible for color encoding are replaced by the bits of the secret message. Analysis of methods of hiding information in graphic files and modeling of algorithms showed an increase in the level of protection of hidden information from detection. Results and their significance: Using the proposed steganography scheme and the algorithm for embedding bytes of a secret message in a graphic file, protection against detection of hidden information is significantly increased. The advantage of this steganography scheme is that for decryption, a key image is used, in which random bytes are pre-embedded. In addition, the entire pixel bits of the container image are used to display the color shades. It can also be noted that the developed steganography scheme allows not only to transmit secret information, but also to add digital fingerprints or hidden tags to the image.

To describe patterns of cannabis use, the degree of overlap between medicinal and recreational users, and their differential use patterns, modes of consumption and sources of cannabis. An ongoing probability-based internet panel maintained by the market research firm GfK Group. Households in Colorado, Washington, Oregon and New Mexico, USA. A total of 2009 individuals from Washington (n=787), Oregon (n=506), Colorado (n=503) and New Mexico (n=213). Post stratification sampling weights were provided so that estimates could be made representative of the household population in each of these states. Respondents were aged between 18 and 91years, with a mean age of 53years. We compare patterns of cannabis consumption for medicinal and recreational users as well as simultaneous use of alcohol and cannabis. We also examine the extent to which patterns of use differ across states that chose to legalize (Washington and Colorado) and those that did not (New Mexico and Oregon). Rates of life-time medical cannabis use are similar in Colorado and Washington (8.8% and 8.2%) but lower in Oregon and New Mexico (6.5% and 1%). Recreational use is considerably higher than medical use across all states (41%), but highest in Oregon and Washington. Approximately 86% of people who report ever using cannabis for medicinal purposes also use it recreationally. Medical users are more likely to vaporize and consume edibles and report a higher amount (in grams) consumed, and spend more money per month than recreational users. Individuals who use cannabis do not commonly use it with alcohol, irrespective of whether they are consuming cannabis recreationally or medically. Fewer than one in five recreational users report simultaneous use of alcohol and cannabis most or all of the time and fewer than 3% of medicinal users report frequent simultaneous use of alcohol and cannabis. In the United States, the degree of overlap between medicinal and recreational cannabis users is 86%. Medicinal and recreational cannabis users favor different modes and amounts of consumption. Only a small proportion (12%) of cannabis users usually consume cannabis and alcohol simultaneously, while concurrent use is common among recreational users.

Background: Concurrent and/or simultaneous use of opioids and benzodiazepines has been associated with increased risk of accident and injury, as well as with co-occurring psychopathology. Objectives: The purpose of the present study was to explore potential correlates of simultaneous opioid and benzodiazepine use in a small community, including perceived risk, positive screens for psychiatric symptoms, and opioid-related consequences. Methods: A sample of 267 participants were recruited from a medication treatment provider that serves a small Midwestern community. Multinomial logistic regression was used to explore demographic and mental health correlates associated with self-reports of past-year simultaneous use. Zero-inflated Poisson regression was used to explore past-year consequences associated with reported simultaneous benzodiazepine and opioid use. Results: Intentional simultaneous use of opioids and benzodiazepines was associated with greater anxiety and depression symptoms, greater likelihood of a positive PTSD screen, and low self-perceived risk of simultaneous use. Individuals reporting opioid/benzodiazepine simultaneous use were also more likely to report opioid-related consequences. Conclusions: Results highlight the importance of assessing and treating simultaneous opioid/benzodiazepine co-use, as well as relevant comorbidities.

Objective: To comprehensively capture intra-tumor heterogeneity in head and neck cancer (HNC) and maximize the use of valid information collected in the clinical field, we propose a novel multi-modal image-text fusion strategy aimed at improving prognosis. Method: We have developed a tailored diagnostic algorithm for HNC, leveraging a deep learning-based model that integrates both image and clinical text information. For the image fusion part, we used the cross-attention mechanism to fuse the image information between PET and CT, and for the fusion of text and image, we used the Q-former architecture to fuse the text and image information. We also improved the traditional prognostic model by introducing time as a variable in the construction of the model, and finally obtained the corresponding prognostic results. Result: We assessed the efficacy of our methodology through the compilation of a multicenter dataset, achieving commendable outcomes in multicenter validations. Notably, our results for metastasis-free survival (MFS), recurrence-free survival (RFS), overall survival (OS), and progression-free survival (PFS) were as follows: 0.796, 0.626, 0.641, and 0.691. Our results demonstrate a notable superiority over the utilization of CT and PET independently, and exceed the result derived without the clinical textual information. Conclusions: Our model not only validates the effectiveness of multi-modal fusion in aiding diagnosis, but also provides insights for optimizing survival analysis. The study underscores the potential of our approach in enhancing prognosis and contributing to the advancement of personalized medicine in HNC.

International audience

Power has become a critical concern for battery-driven computing systems, on which many applications that are run are interactive. System-level voltage scaling techniques, such as dynamic voltage scaling (DVS) and adaptive body biasing (ABB), have been shown to reduce energy consumption effectively. Previous works on DVS and ABB exploit low CPU utilization of the processor to drive voltage scaling. This has become inadequate for modern interactive applications involving high CPU usage. In this work, we target computer responsiveness during voltage scaling to exploit more opportunities for energy reduction. Instead of CPU utilization, we use the user-perceived latency, the delay between user input and computer response, to drive voltage scaling. Considering the tradeoff between energy consumption and computer responsiveness during voltage scaling not only reduces energy consumption effectively, but also ensures good computer responsiveness for interactive applications. Experimental results show that for the 70nm technology, during the execution of seven commonly-used interactive applications, the energy consumption of the processor using user-perceived latency driven DVS is reduced by an average of 37.3%, and the user-perceived latency by an average of 18.3%, compared to CPU utilization driven DVS. If both DVS and ABB are performed simultaneously based on the user-perceived latency, then the energy consumption is reduced by another 38.9% compared to when DVS is performed alone, while maintaining a similar computer responsiveness level. We have implemented user-perceived latency driven voltage scaling under Linux with X Window system. However, the methodology is extensible to other operating systems as well.

Many important information systems applications require access to data stored in multiple heterogeneous databases. This paper examines a problem in inter-database data manipulation within a heterogeneous environment, where conventional techniques are no longer useful. To solve the problem, a broader definition for join operator is proposed. Also, a method to probabilistically estimate the accuracy of the join is discussed.

The Carnot project was an ambitious research project in heterogeneous databases. It integrated a variety of techniques to address a wide range of problems in achieving interoperation in heterogeneous environments. Here we describe some of the major implemented applications of this project. These applications concern (a) accessing a legacy scientific database, (b) automating a workflow involving legacy systems, (c) cleaning data, and (d) retrieving semantically appropriate information from structured databases in response to text queries. These applications support scientific decision support, business process management, data integrity enhancement, and analytical decision support, respectively. They demonstrate Carnot‘s capabilities for (a) heterogeneous query processing, (b) relaxed transaction and workflow management, (c) knowledge discovery, and (d) heterogeneous resource model integration.

The Internet of Things (IoT) is a modern paradigm consisting of heterogeneous intercommunicated devices that sending and receiving messages in various formats through different protocols. Thanks to the everywhere use of smart things, it is becoming common to collect large quantities of data generated by resource-constrained, distributed devices at one or more servers. However, the wireless transmitting of data is very expensive. For example in IoT, using Bluetooth Low Energy costs tens of millijoules per connection, while computing at full energy costs only tens of microjoules, and sitting idle costs close to one microjoule per second for STM processors. That is why additional data compression for smart devices can decrease the energy costs of IoT. There are methods of data compression without or with information loss. It is mathematically proved, that it is possible to construct as arbitrarily close approximations of a weighted sum of generalized orthogonal polynomials to an input function (IoT data). In this article, we are researching the Chebyshev and Fourier sequences as an approximation of source data. For a different type of data in the different sequences, we have a different compression for Chebyshev and Fourier approximation. Concurrent use of transformations allows selecting a maximal compression for different sequences. This article proposes a compression method especially suited for IoT devices. The proposed method is based on the simultaneous use of Chebyshev and Fourier transforms. To improve the compression performance was used a trigonometric optimization. The modification of Chebyshev transformation allows reducing energy costs by about four times. Trigonometric optimization replaces the direct use of the mathematical function cos(x) in a double loop by iteration expressions. A modified algorithm uses a one-time calculation of the cos(x) function. As a result, we have a slight increase of the source code and decrease of the computation time, and increasing energy effectiveness. The software implementation in C ++ of the modified Chebyshev transformation algorithm was proposed. The proposed method can be used not only in IoT but also for the accumulation of data on big servers.

The Clinical Significance of Retarded Hepatic Drug Metabolism

A safe and effective colorectal cancer chemoprevention agent remains to be discovered. There is little evidence regarding the protective effect of chondroitin sulphate and glucosamine on colorectal cancer. We aimed to assess the association between colorectal cancer risk and the use of chondroitin sulphate and glucosamine using a large cohort with dispensed data. We performed a population-based case-control study in Catalonia using primary care reimbursed medication records (SIDIAP database). The study included 25,811 cases with an incident diagnosis of colorectal cancer and 129,117 matched controls between 2010 and 2015. The prevalence of ever use was 9.0% (n = 13,878) for chondroitin sulphate, 7.3% (n = 11,374) for glucosamine, and 35% for regular use of nonsteroidal anti-inflammatory drugs (NSAID; n = 45,774). A decreased risk of colorectal cancer was observed among chondroitin sulphate use [OR: 0.96; 95% confidence interval (CI), 0.91-1.01], glucosamine use (OR: 0.92; 95% CI, 0.87-0.97), and concurrent use of chondroitin sulphate and glucosamine (OR: 0.83; 95% CI, 0.70-0.98). Especially for glucosamine, there was a dose-response association regarding duration and cumulative dose. The analysis stratified by simultaneous use with other NSAIDs showed that these drugs used without other NSAIDs do not reduce risk (OR: 1.06; 95% CI, 0.74-1.51). However, they may have a synergistic protective effect when used with other NSAIDs (OR: 0.80; 95% CI, 0.72-0.88). This study does not provide strong support for an independent protective association of chondroitin sulphate or glucosamine on colorectal cancer risk in our population. However, these drugs may have a synergistic beneficial effect among NSAID users. Chondroitin sulphate or glucosamine may contribute to the protective effect of NSAID use in colorectal cancer.

Mendelsohn highlights an important point.1 2 There are valid reasons to think that patches may be more useful prequit than other forms of nicotine replacement therapy (NRT). Research has shown...