Psychosocial correlates of attitudes towards male sexual violence in a sample of financial crime, property crime, general violent, and homicide offenders

Whilst those currently serving prison sentences for sexual violence can be identified and receive treatment, the number of prisoners with a history of sexual violence against female partners is unknown. Methods to identify prisoners with a proclivity for such violence and accurately assess the risk they pose before and after incarceration are therefore required. Here, we aimed to assess the level of sexually violent attitudes within dating relationships and to examine their associations with experiences of child abuse and neglect (CAN), psychopathic personality traits, prisonization, number of incarcerations, age, years of schooling, relationship status, and parenting among different types of offenders (financial crime, property crime, general violent, and homicide offenders). Data were collected among a large systematically selected sample of adult male inmates (N = 1,123). We demonstrated that sexual violence-supportive attitudes appear to be a function of child sexual abuse, psychopathic personality traits, and may be developed through early socialisation experiences as well as incarceration. Practical implications of current findings are discussed

An exploratory analysis of planning characteristics in Australian visitor attractions

This paper provides an exploratory analysis of the planning practices of 408 Australian attraction operators. The results indicate that attraction managers can be divided into four categories: those that do not engage in any formal planning, those that adopt a short-term planning approach, those that develop long-term plans, and those that use both short-term and long-term planning approaches. An evaluation of the sophistication of attraction planning showed a bipolar distribution. Attraction managers favored a planning horizon of three or five years, and were inclined to involve their employees in the planning process. Managers relied strongly on their own research and tourism industry intelligence when formulating business plans. The content of plans tended to focus on operational activities, financial planning and marketing. The study provides a benchmark for the comparison of attraction planning efforts in various contexts. © 2006 Asia Pacific Tourism Association

Electromagnetic Wave Theory and Applications

Contains table of contents for Section 3, reports on six research projects and a list of publications and conference papers.Joint Services Electronics Program Contract DAAL03-89-C-0001National Science Foundation Grant ECS 86-20029Schlumberger- Doll ResearchU.S. Army Research Office Contract DAAL03 88-K-0057U.S. Navy - Office of Naval Research Contract N00014-90-J-1002National Aeronautics and Space Administration Grant NAGW-1617U.S. Navy - Office of Naval Research Grant N00014-89-J-1107National Aeronautics and Space Administration Grant NAGW-1272National Aeronautics and Space Administration Agreement 958461U.S. Army - Corps of Engineers Contract DACA39-87-K-0022U.S. Air Force - Electronic Systems Division Contract F19628-88-K-0013U.S. Navy - Office of Naval Research Grant N00014-89-J-1019Digital Equipment CorporationIBM CorporationU.S. Department of Transportation Contract DTRS-57-88-C-00078Defence Advanced Research Projects Agency Contract MDA972-90-C-002

Association of Current Opioid Use With Serious Adverse Events Among Older Adult Survivors of Breast Cancer

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Importance National efforts to improve safe opioid prescribing focus on preventing misuse, overdose, and opioid use disorder. This approach overlooks opportunities to better prevent other serious opioid-related harms in complex populations, such as older adult survivors of cancer. Little is known about the rates and risk factors for comprehensive opioid-related harms in this population. Objective To determine rates of multiple opioid-related adverse drug events among older adults who survived breast cancer and estimate the risk of these events associated with opioid use in the year after completing cancer treatment. Design, Setting, and Participants This retrospective cohort study used 2007 to 2016 Surveillance, Epidemiology and End Results-Medicare data from fee-for-service Medicare beneficiaries with first cancer diagnosis of stage 0 to III breast cancer at age 66 to 90 years from January 1, 2008, through December 31, 2015, who completed active breast cancer treatment. Data were analyzed from October 31, 2019, to June 10, 2020. Exposures Repeated daily measure indicating possession of any prescription opioid supply in Medicare Part D prescription claims. Main Outcomes and Measures Adjusted risk ratios (aRRs), estimated using modified Poisson generalized estimating equation models, for adverse drug events related to substance misuse (ie, diagnosed opioid abuse, dependence, or poisoning), other adverse drug events associated with opioid use (ie, gastrointestinal events, infections, falls and fractures, or cardiovascular events), and all-cause hospitalization associated with opioid supply the prior day, controlling for patient characteristics. Results Among 38 310 women included in the study (mean [SD] age, 74.3 [6.3] years), there were 0.010 (95% CI, 0.008-0.011) adverse drug events related to substance misuse per 1000 person-days, 0.237 (95% CI, 0.229-0.245) other adverse drug events associated with opioid use per 1000 person-days, and 0.675 (95% CI, 0.662-0.689) all-cause hospitalizations per 1000 person-days. Opioid use was associated with increased risk of adverse drug events related to substance misuse (aRR, 14.62; 95% CI, 9.69-22.05; P < .001), other adverse drug events related to opioid use (aRR, 2.50; 95% CI, 2.11-2.96; P < .001), and all-cause hospitalization (aRR, 2.77; 95% CI, 2.55-3.02; P < .001). In a dose-response effect, individuals with high daily opioid doses had consistently higher risks of all study outcomes compared with individuals who had low opioid doses. Compared with days with no opioid exposure, the risk of any adverse drug event related to substance misuse was 3.4-fold higher for individuals with a current opioid supply ≥50 mg morphine equivalent dose per day (aRR, 3.40; 95% CI, 2.47-4.68; P < .001), while the risk was 2.3-fold higher for individuals with 1 to 49 mg morphine equivalent dose per day (aRR, 2.29; 95% CI, 1.89-2.77; P < .001). Conclusions and Relevance These findings suggest that among older adults who survived breast cancer, continued prescription opioid use in the year after completing active cancer treatment was associated with an immediate increased risk of a broad range of serious adverse drug events related to substance misuse and other adverse drug events associated with opioid use. Clinicians should consider the comprehensive risks of managing cancer pain with long-term opioid therapy

Challenges and recent progress in drug discovery for tropical diseases

Infectious tropical diseases have a huge effect in terms of mortality and morbidity, and impose a heavy economic burden on affected countries. These diseases predominantly affect the world’s poorest people. Currently available drugs are inadequate for the majority of these diseases, and there is an urgent need for new treatments. This Review discusses some of the challenges involved in developing new drugs to treat these diseases and highlights recent progress. While there have been notable successes, there is still a long way to go.</p

Simultaneous identification of long similar substrings in large sets of sequences

<p>Abstract</p> <p>Background</p> <p>Sequence comparison faces new challenges today, with many complete genomes and large libraries of transcripts known. Gene annotation pipelines match these sequences in order to identify genes and their alternative splice forms. However, the software currently available cannot simultaneously compare sets of sequences as large as necessary especially if errors must be considered.</p> <p>Results</p> <p>We therefore present a new algorithm for the identification of almost perfectly matching substrings in very large sets of sequences. Its implementation, called ClustDB, is considerably faster and can handle 16 times more data than VMATCH, the most memory efficient exact program known today. ClustDB simultaneously generates large sets of exactly matching substrings of a given minimum length as seeds for a novel method of match extension with errors. It generates alignments of maximum length with a considered maximum number of errors within each overlapping window of a given size. Such alignments are not optimal in the usual sense but faster to calculate and often more appropriate than traditional alignments for genomic sequence comparisons, EST and full-length cDNA matching, and genomic sequence assembly. The method is used to check the overlaps and to reveal possible assembly errors for 1377 <it>Medicago truncatula </it>BAC-size sequences published at <url>http://www.medicago.org/genome/assembly_table.php?chr=1</url>.</p> <p>Conclusion</p> <p>The program ClustDB proves that window alignment is an efficient way to find long sequence sections of homogenous alignment quality, as expected in case of random errors, and to detect systematic errors resulting from sequence contaminations. Such inserts are systematically overlooked in long alignments controlled by only tuning penalties for mismatches and gaps.</p> <p>ClustDB is freely available for academic use.</p

Immersion Lithography Defectivity Analysis at DUV Inspection Wavelength

ABSTRACT Significant effort has been directed in recent years towards the realization of immersion lithography at 193nm wavelength. Immersion lithography is likely a key enabling technology for the production of critical layers for 45nm and 32nm design rule (DR) devices. In spite of the significant progress in immersion lithography technology, there remain several key technology issues, with a critical issue of immersion lithography process induced defects. The benefits of the optical resolution and depth of focus, made possible by immersion lithography, are well understood. Yet, these benefits cannot come at the expense of increased defect counts and decreased production yield. Understanding the impact of the immersion lithography process parameters on wafer defects formation and defect counts, together with the ability to monitor, control and minimize the defect counts down to acceptable levels is imperative for successful introduction of immersion lithography for production of advanced DR&apos;s. In this report, we present experimental results of immersion lithography defectivity analysis focused on topcoat layer thickness parameters and resist bake temperatures. Wafers were exposed on the 1150i-α-immersion scanner and 1200B Scanner (ASML), defect inspection was performed using a DUV inspection tool (UVision TM , Applied Materials). Defect material analysis was performed for different defects. Higher sensitivity was demonstrated at DUV through detection of small defects not detected at the visible wavelength, indicating on the potential high sensitivity benefits of DUV inspection for this layer. The analysis indicates that certain types of defects are associated with different immersion process parameters. This type of analysis at DUV wavelengths would enable the optimization of immersion lithography processes, thus enabling the qualification of immersion processes for volume production

Uncovering the environmental conditions required for Phyllachora maydis infection and tar spot development on corn in the United States for use as predictive models for future epidemics

Phyllachora maydis is a fungal pathogen causing tar spot of corn (Zea mays L.), a new and emerging, yield-limiting disease in the United States. Since being first reported in Illinois and Indiana in 2015, P. maydis can now be found across much of the corn growing regions of the United States. Knowledge of the epidemiology of P. maydis is limited but could be useful in developing tar spot prediction tools. The research presented here aims to elucidate the environmental conditions necessary for the development of tar spot in the field and the creation of predictive models to anticipate future tar spot epidemics. Extended periods (30-day windowpanes) of moderate mean ambient temperature (18–23 °C) were most significant for explaining the development of tar spot. Shorter periods (14- to 21-day windowpanes) of moisture (relative humidity, dew point, number of hours with predicted leaf wetness) were negatively correlated with tar spot development. These weather variables were used to develop multiple logistic regression models, an ensembled model, and two machine learning models for the prediction of tar spot development. This work has improved the understanding of P. maydis epidemiology and provided the foundation for the development of a predictive tool for anticipating future tar spot epidemics