Short-term Users Research Articles

Surveying digital musical instrument use in active practice

Digital musical instruments are frequently designed in research and experimental performance contexts but few are taken up into sustained use by active and professional musicians. To identify the needs of performers who use novel technologies in their practices, a survey of musicians was conducted that identified desirable qualities for instruments to be viable in active use, along with attributes for successful uptake and continued use of instruments based on frameworks of long and short term user engagement. The findings are presented as a set of design considerations towards the development of instruments intended for use by active and professional performers.

A Convolutional Attention Network for Unifying General and Sequential Recommenders

General recommenders and sequential recommenders are two modeling paradigms of recommender. The main focus of a general recommender is to identify long-term user preferences, while the user’s sequential behaviors are ignored and sequential recommenders try to capture short-term user preferences by exploring item-to-item relations, failing to consider general user preferences. Recently, better performance improvement is reported by combining these two types of recommenders. However, most of the previous works typically treat each item separately and assume that each user–item interaction in a sequence is independent. This may be a too simplistic assumption, since there may be a particular purpose behind buying the successive item in a sequence. In fact, a user makes a decision through two sequential processes, i.e., start shopping with a particular intention and then select a specific item which satisfies her/his preferences under this intention. Moreover, different users usually have different purposes and preferences, and the same user may have various intentions. Thus, different users may click on the same items with an attention on a different purpose. Therefore, a user’s behavior pattern is not completely exploited in most of the current methods and they neglect the distinction between users’ purposes and their preferences. To alleviate those problems, we propose a novel method named, CAN, which takes both users’ purposes and preferences into account for the next-item recommendation. We propose to use Purpose-Specific Attention Unit (PSAU) in order to discriminately learn the representations of user purpose and preference. The experimental results on real-world datasets demonstrate the advantages of our approach over the state-of-the-art methods.

Attention-based dynamic user modeling and Deep Collaborative filtering recommendation

Deep learning (DL) techniques have been widely used in recommender systems for user modeling and matching function learning based on historical interaction matrix. However, existing DL-based recommendation methods usually perform static user preference modeling by using historical interacted items of the user. In this article, we present a time-aware deep CF framework which contains two stages: dynamic user preference modeling based on attention mechanism and matching score prediction based on DL. In the first stage, short-term user preferences are modeled by the time-aware attention mechanism that fully considered the predicted item, the recent interacted items and their interaction time. The resulting short-term preferences are combined with long-term preferences for dynamic user preference modeling. In the second stage, high-order user-item feature interactions are learned by two types of DL models, Deep Matrix Factorization (DMF) and Multiple-Layer Perception (MLP), and the feature interaction vectors of the two models are fused in the last layer of the model to predict the matching score. Extensive experiments on five datasets indicate that our method is superior to the existing time-aware and DL-based recommendation methods in top-k recommendations significantly and consistently.

Real-time POI recommendation via modeling long- and short-term user preferences

Recently, Next Point-of-Interest (POI) Recommendation which proposes users for their next visiting locations, has gained increasing attention. A timely and accurate next POI recommendation can improve users’ efficient experiences. However, most existing methods typically focus on the sequential influence, but neglect the user’s real-time preference changing over time. In some scenarios, users may need a real-time POI recommendation, for example, when using Take-away Applications, users need recommending the appropriate restaurants at the specific moment. Hence, how to mine users’ patterns of life and their current preferences becomes an essential issue for the real-time POI recommendation. To address the issues above, we propose a real-time preference mining model (RTPM) which is based on LSTM to recommend the next POI with time restrictions. Specifically, RTPM mines users’ real-time preferences from long-term and short-term preferences in a uniform framework. For the long-term preferences, we mine the periodic trends of users’ behaviors between weeks to better reflect users’ patterns of life. While for the short-term preferences, trainable time transition vectors which represent the public preferences in corresponding time slots, are introduced to model users’ current time preferences influenced by the public. At the stage of recommendation, we design a category filter to filter out the POIs whose categories are unpopular in corresponding time slots to reduce the search space and make recommendation fit current time slot better. Note that RTPM does not utilize users’ attributes and their current locations for recommendation, which makes great contributions to users’ privacy protection. Extensive experiments on two real-world datasets demonstrate that RTPM outperforms the state-of-the-art models on Recall and NDCG.

Long-Term Recreational Cannabis Use Is Associated With Lower Executive Function and Processing Speed in a Pilot Sample of Older Adults.

More older adults are using cannabis for recreational and/or medical purposes, but most studies examining cognitive function and cannabis use do not include older adults. The current small pilot study sought to compare cognitive function and emotional functioning among adults age 60 and older who were regular, primarily recreational cannabis users (n = 28) and nonusers (n = 10). A bimodal distribution was observed among cannabis users such that they had either initiated regular use more recently ("short-term" users; ≤7 years, n = 13) or earlier in life ("long-term" users; ≥19 years, n = 15). Nonusers, short-term, and long-term users were not different in depression, anxiety, or emotion regulation, or alcohol use. Nonusers scored significantly higher than long-term users in executive function. Short-term users scored significantly higher than long-term users in executive function, processing speed, and general cognition. Additionally, greater recent cannabis use frequency was negatively associated with working memory. The current findings suggest that short-term recreational cannabis use does not result in differences in cognitive performance compared to nonusers, which may indicate that short-term use is relatively benign in older adults. However, longer duration of use is associated with poorer processing speed and executive functioning, and more recent cannabis use is associated with poorer working memory, which may impact older adults' overall cognitive functioning.

A multi-task learning approach for improving travel recommendation with keywords generation

Travel recommendation is very critical to helping users quickly find products or services that they are interested in. The key to travel recommender systems is learning user shopping intentions, which are expressed through various supervision signals, such as the clicked products and their titles. Existing travel recommendation methods commonly infer user intentions from click behaviors on travel products. However, remarkable keywords in the product title, such as departure, destination, travel time, hotel, and transportation are paid less attention. To this end, we hypothesize that modeling click sequences and product keywords in title jointly would result in a more holistic representation of a product and towards more accurate recommendations. Thus, we propose a TRKG (short for Travel Recommendation with Keywords Generation) model, which fulfills the travel recommendation and keywords generation tasks simultaneously. To generate explainable outputs, unlike most previous approaches that regard the product title as a hidden feature vector, TRKG regards keywords in the product title as an additional supervision signal. Meanwhile, TRKG integrates the long-term and short-term user preferences in the travel recommendation component and the keywords generation component. To evaluate the proposed model, we constructed datasets from a large tourism e-commerce website in China. Extensive experiments demonstrate that the proposed method yields significant improvements over state-of-the-art methods.

A Survey on Session-based Recommender Systems

Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users’ clicks on items), and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.

Abstract 825: Genetically determined elevated C-reactive protein levels and chronic inflammation status associated with primary colorectal cancer risk: Mendelian randomization with lifestyle interactions

Abstract Purpose: Immune-related etiologic pathways in the development of colorectal cancer (CRC) have not been convincingly determined and may be confounded by lifestyle factors or reverse causality. We investigated the genetically predicted C-reactive protein (CRP) phenotype in the potential causal pathway of primary CRC risk in postmenopausal women in a Mendelian randomization (MR) framework. Methods: We employed individual-level data of the Women's Health Initiative Database for Genotypes and Phenotypes Study, which consists of 5 genome-wide association (GWA) studies, including 10,142 women, 737 of whom developed primary CRC. We examined 61 GWA single-nucleotide polymorphisms (SNPs) associated with CRP by using weighted/penalized MR weighted-medians and MR gene-environment interactions that allow some relaxation of the strict variable requirements and attenuate the heterogeneous estimates of outlying SNPs. Results: In lifestyle-stratification analyses, genetically determined CRP exhibited its effects on the decreased CRC risk in non-viscerally obese and high-fat diet subgroups. In contrast, genetically driven CRP was associated with an increased risk for CRC in women who smoked 15 or more cigarettes/day, with significant interaction of the gene-smoking relationship. Further, a substantially increased risk of CRC induced by CRP was observed in relatively short-term users (less than 5 years) of estrogen (E)-only and also longer-term users (longer than 5 years) of E plus progestin. Conclusions: Our findings may provide novel evidence on immune-related etiologic pathways connected to CRC risk and suggest the possible use of CRP as a CRC-predictive biomarker in women with particular behaviors and CRP marker-informed interventions to reduce CRC risk. Citation Format: Su Yon Jung, Herbert Yu, Matteo Pellegrini, Jeanette C. Papp, Eric M. Sobel, Zuo-Feng Zhang. Genetically determined elevated C-reactive protein levels and chronic inflammation status associated with primary colorectal cancer risk: Mendelian randomization with lifestyle interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 825.

Chronic Pregabalin Treatment and Oxycodone Requirement after Spinal Surgery Versus Short Course Perioperative Administration: A Prospective, Nonrandomized Study

Although being controversial, pregabalin (PGB) is proposed during a short perioperative period to improve pain relief.Comparisons between chronic and short-term users during lumbar spine surgery are lacking. The purpose was to compare opioid requirements and postoperative pain among PGB chronic users and naive patients receiving a 48-hour perioperative administration. Prospective nonrandomized study. Tertiary care hospital. Chronic users (group PGB, n = 39) continued their treatment, naive patients (group C, n = 43) received a dose of 150 mg preoperatively and 75 mg/12 hours for 48 hours. Anesthesia and analgesia were standardized. The primary outcome was the cumulative oxycodone consumption at 24 hours, other outcomes included pain scores, DN4 (Douleur Neuropathique 4 Questions) scores, and side effects. Group PGB consumed less oxycodone at 24 hours (median [interquartile range] 10 mg [10-17.5] vs. 20 mg [10-20], P = 0.013], at 48 hours (15 mg [10-20] vs. 20 mg [12.5-30], P = 0.018), and required less intraoperative remifentanil (P = 0.004). Both groups showed similar pain scores during the 48-hour follow-up and at 3 months.Based on multivariate analysis, chronic users of PGB before surgery exhibited lower oxycodone requirements at 24 hours (odds ratio, 3.98; 95% confidence interval, 1.44-7.74; P = 0.008]. No differences were noted regarding side effects and DN4 scores. Nonrandomized study. Patients chronically treated with PGB required less opioid when compared with a short perioperative administration before spinal surgery. Further prospective studies are required to confirm these results in spinal surgeries.

ADCF: Attentive representation learning and deep collaborative filtering model

In this paper, we propose a deep collaborative filtering recommendation model, which consists of an attention-based representation learning component and a multi-input matching function learning component. This model takes interaction matrix based on implicit feedback as data source to construct representations of long-term user preferences and item latent features. In the representation learning, a time-aware attention network is used, which uses the embedding vectors of the predicted item, recent historical interaction items, and the interaction time of recent historical interaction items to estimate the weights of different historical interaction items to short-term user preferences modeling. Then, the dynamic user preference representation can be obtained by combining short-term preferences with long-term preferences. In the matching function learning, a multi-input deep learning model is used. Its input includes not only the dynamic user preference representation and the item latent feature representation, but also the linear interaction between the two representations, so that the model has more powerful feature interactions learning ability. Experimental results on four datasets from different domains show that our method is largely superior to the state-of-the-art collaborative filtering methods, and the novel techniques we propose in this paper are effective in improving recommendation performance.

Who Experience Out-of-Pocket Expenditures for Modern Contraceptive Use in Indonesian Universal Health Coverage System?

Integration of family planning programs into the universal health coverage system is expected to improveaccess toreproductive health services. All people should have access to sufficient and qualified family planningservices they need without financial hardship. This study aimed to examine out-of-pocket expenditures ofcontraceptive services and the associated factors among fertile age couples that influence their access tomodern contraceptive services.It was quantitative research with a cross-sectional design. Data obtained fromthe Health and Demographic Surveillance System 2016 of Sleman Regency, Indonesia, were analyzed usingmultiple logistic regression to determine factors associated with out-of-pocket expenditures for contraceptiveservices. This study revealed that more than 70% of people should pay for contraceptive services eventhough they had registered as health insurance members. Public non-subsidized and private health insurancemembers were more likely to experience OOP expenditures than public-subsidized participants (AOR=3.12;95%CI=2.25-4.30 and AOR=3.47; 95%CI=1.60-7.52, respectively). Short-term contraception users weremore likely to experience out-of-pocket expenditures thanlong-term users(AOR=6.38; 95%CI=4.79-8.50).Nearly three-quarters of health insurance participants experience out-of-pocket expenditures forcontraceptive services. Out-of-pocket expenditures of contraceptive services significantly associate with thetype of health insurance owned and the types of contraceptive methods used.

629-P: Real-World Evidence of Diabetes App Use in Daily Life: Assessing New Users’ Engagement with a Blood Glucose Meter App over a Four-Month Period

Background: Blood glucose (BG) meter apps facilitate the recording and review of BG data with the goal of improving diabetes self-management. Yet, while these apps require user engagement to be effective, there have been few studies exploring how people with diabetes use their apps over time. In the REALL study, we followed a group of new BG meter app users to determine how long they used their app, and how frequently they used their app to record a BG reading. Methods: From March-July 2020, adults with diabetes who had recently paired their CONTOUR®NEXT ONE (CNO) meter with the CONTOUR®DIABETES app were enrolled in the study. All participants completed 2-3 online surveys, recorded ≥1 BG reading in their app, and shared their app data for ≥16 weeks. For each participant, ‘period of app use’ was defined as the time between one’s pair date and the date of their final recorded BG reading. Within one’s period of use, ‘frequency of BG recordings’ was defined as the percent of days with ≥1 BG reading. Results: Participants (n=488) were 51.4 ± 11.9 years old, 52% were male, and 95% had type 2 diabetes. Based on their period of use, 78% of participants were classified as long-term app users (>4 weeks), 11% as medium-term users (>1 week, ≤4 weeks), and 11% as short-term users (≤1 week). Long-term app users were further characterized by their frequency of BG recordings, with 57% classified as high frequency users (BG on ≥70% of days), 23% as medium frequency users (BG on 40-69.9% of days), and 21% as low frequency users (BG on <40% of days). Most participants expressed high satisfaction (9 or 10 rating on 10-point scale) with the CNO meter (83%) and app (68%). Conclusions: Over the course of four months, participants demonstrated sustained and consistent use of the CONTOUR®DIABETES app to record BG readings. These findings provide evidence of high levels of app engagement in a real-world setting, and further testify to the potential for BG meter apps to support diabetes self-management. Disclosure R. Wood: Research Support; Self; Abbott Diabetes, American Diabetes Association, Ascensia Diabetes Care, Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk Inc., Sanofi. C. Florissi: Other Relationship; Self; Abbott Diabetes, Ascensia Diabetes Care, Dexcom, Inc., Insulet Corporation, LifeScan, Lilly Diabetes, Medtronic, Roche Diabetes Care, Senseonics, Tandem Diabetes Care. M. Younge: None. K. C. Stoner: Other Relationship; Self; Several companies in the diabetes field. L. Fisher: Consultant; Self; Ascensia Diabetes Care, Lilly Diabetes, Speaker’s Bureau; Self; Dexcom, Inc. J. Knaebel: Employee; Self; Ascensia Diabetes Care. C. T. Lu: Employee; Self; Ascensia Diabetes Care, Employee; Spouse/Partner; Engineering Resource Group. A. Stuhr: Employee; Self; Ascensia Diabetes Care.

Personalized recommendation system based on knowledge embedding and historical behavior

Collaborative filtering (CF) usually suffers from limited performance in recommendation systems due to the sparsity of user–item interactions and cold start problems. To address these issues, auxiliary information from knowledge graphs, such as social networks and item properties, is typically used to boost performance. The current recommended algorithms based on knowledge graphs fail to utilize rich semantic associations. In this paper, we regard knowledge graphs as heterogeneous networks to add auxiliary information, propose a recommendation system with unified embeddings of behavior and knowledge features, and mine user preferences from their historical behavior and knowledge graphs to provide more accurate and diverse recommendations to the users. Our proposed ReBKC shows a significant improvement on three datasets compared to state-of-the-art methods. These results verify the effectiveness of learning short-term and long-term user preferences from their historical behavior and by integrating knowledge graphs to deeply identify user preferences.

Risk of skin cancer in new users of thiazides and thiazide-like diuretics: a cohort study using an active comparator group.

Case-control studies report a dose-dependent increased risk of skin cancer in users of hydrochlorothiazide (HCTZ) vs. nonusers. The degree to which other thiazides and thiazide-like diuretics (TZs) are associated with skin cancer is less certain. To assess the risk of skin cancer in new users of different TZs compared with new users of calcium channel blockers (CCBs). We conducted a cohort study using a UK primary-care database (1998-2017), including 271154 new TZ users [87·6% bendroflumethiazide (BFT), 5·8% indapamide and 3·6% HCTZ] and 275263 CCB users. The outcomes were basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and cutaneous malignant melanoma (CMM). We estimated incidence rates (IRs) and IR ratios (IRRs) in short-term (< 20 prescriptions) and long-term (≥ 20 prescriptions) users of TZs and CCBs using negative binomial regression, and calculated rate differences (RDs) for selected results. We used fine stratification on the propensity score (PS) to control for 23 baseline covariates. Long-term use of HCTZ increased absolute and relative risks of SCC [PS-weighted IRR 1·95; 95% confidence interval (CI) 1·87-2·02; RD per 100000 person-years 87.4], but not of BCC or CMM. Long-term use of indapamide was associated with an increased incidence of CMM (IRR 1·43; 95% CI 1·35-1·50). BFT was not meaningfully associated with the risk of any type of skin cancer. Our results corroborate the previously reported increased risk of SCC (but not of BCC or CMM) for long-term use of HCTZ. BFT may be a safer alternative for patients at increased risk of skin cancer.

Corn cash price forecasting with neural networks

We explore the forecasting issue in a data set of daily corn cash prices from nearly 500 markets across sixteen states: North Dakota, Iowa, Minnesota, Illinois, Indiana, Ohio, Michigan, Missouri, Nebraska, Arkansas, Kentucky, Wisconsin, South Dakota, Kansas, Oklahoma, and Pennsylvania. We focus on univariate neural network (NN) modeling and bivariate NN modeling with futures prices incorporated. Using simple NNs with twenty hidden neurons and two delays leads to forecasting of high accuracy for the one-day ahead horizon. Including futures prices in the models benefits cash price forecasting. These findings are robust to data splitting ratios for model training, validation, and testing, and to different algorithms employed for model estimates. The forecasting framework here is relatively easy to deploy and has potential to be generalized to other commodities. This study contributes to short-term forecasting users’ information needs in decision making processes.

Statins and risk of diverticular disease: Nested case-control study.

Statins exert pleiotropic anti-inflammatory effects and may prevent diverticular disease. However, the association remains poorly understood with previous studies obtaining conflicting results. To examine the effect of statin on the subsequent risk of diverticular disease. We conducted a nested case-control study in Denmark among respondents (>18 years) of the 2010 or the 2013 Danish National Health Survey. Among these, we identified 8809 cases of hospital-diagnosed diverticular disease and risk-set sampled population controls without diverticular disease. Using complete prescription and hospital records, we used conditional logistic regression to compute odds ratios (ORs) associating statin use with diverticular disease. In adjusted analyses, we controlled for hospital-based diagnoses, medication use other than statins, and lifestyle and socioeconomic factors. The fully adjusted OR for diverticular disease associated with ever use (≥1 statin prescription filling) was 1.19 (95% CI: 1.12-1.27) compared with never use. However, we observed no dose-response relation. For example, among short-term users (<5 years), the OR was 1.18 (95% CI: 1.04-1.35) for low intensity users and 1.13 (95% CI: 1.01-1.26) for high intensity users. Among long-term users (≥5 years), the respective ORs were 1.25 (95% CI: 1.13-1.38) and 1.11 (95% CI: 0.98-1.24). In analyses restricting to cases and controls with a previous colonoscopy, associations were null (OR: 1.01 [95% CI: 0.85-1.20]). The observed association of a higher risk of diverticular disease associated with statins could be explained by diagnostic bias. Our study did not support a protective nor harmful effect of statins on the risk of diverticular disease.

Long-term treatment of endometriosis with dienogest: Real-world results from the VIPOS study

Introduction: The Visanne Post-approval Observational Study (VIPOS) was designed to assess the safety of dienogest 2 mg (DNG, Visanne) compared to other hormonal endometriosis treatments. Methods: Large, prospective, non-interventional, active surveillance study in six European countries (Germany, Poland, Russia, Hungary, Switzerland, and Ukraine). Women with a new hormonal therapy for endometriosis were enrolled by gynecologists and specialized centers between 2010 and 2016 and observed for up to 7 years. Self-administered questionnaires during study entry and follow-up collected information on baseline characteristics, health status and endometriosis treatment. Self-reported clinical outcomes of interest were validated by health care professionals. Results: Among the &gt;27,000 enrolled participants, 3262 women started DNG use either at study entry or during follow-up. A total of 798 study participants used DNG during follow-up continuously for 15 months or longer (DNG long-term users). When comparing the occurrence of serious adverse events (SAE) in users treated with DNG, no safety signal emerged for long-term users; the SAE incidence rate per 10,000 women-years was 367.7 (95% CI: 274.1–481.9) in DNG long-term users and 416.4 (349.1–492.5) in short-term users (treated with DNG for less than 15 months). Conclusions: Previous data on DNG long-term safety were derived from studies with relatively low numbers of patients and limited follow-up time. VIPOS provided valuable real-world data on the long-term use of DNG 2 mg in around 800 women treated in Europe and observed no safety signal regarding serious adverse events.

Traditional Chinese Medicine Enhances Survival in Patients with Gastric Cancer after Surgery and Adjuvant Chemotherapy in Taiwan: A Nationwide Matched Cohort Study

Background Gastric cancer remains a major cancer globally. More than half of patients with gastric cancer undergo surgery in Taiwan; however, few large nationwide studies have investigated the effects of traditional Chinese medicine (TCM) on gastric cancer management after surgery. This study aimed to evaluate the effect of TCM on patients with gastric cancer following surgery and adjuvant chemotherapy in Taiwan and its prescription trends. Methods and Materials The cohort sampling data set was obtained from the Registry of Catastrophic Illness Patient Database, a research database of patients with severe illnesses from the National Health Insurance Research Database, Taiwan. Patients who had received a new diagnosis of gastric cancer and had undergone surgery were enrolled. We matched TCM users and nonusers at a ratio of 1 : 3 based on the propensity score, and TCM users were also grouped into short-term and long-term users. Results The number of TCM users and nonusers was 1701 and 5103 after applying the propensity score at a ratio of 1 : 3. Short-term users and long-term TCM users were independently associated with a decreased risk of death with HRs of 0.59 (95% confidence interval (CI), 0.55–0.65) and 0.41 (95% CI, 0.36–0.47), respectively, compared with TCM nonusers. We also obtained similar results when we adjusted for covariates in the main model, as well as each of the additional listed covariates. We also observed similar HR trends in short-term users and long-term TCM users among men and women aged <65 years and ≥65 years. The most commonly prescribed single herb and herbal formula in our cohort were Hwang-Chyi (Radix Hedysari; 11.8%) and Xiang-Sha-Liu-Jun-Zi-Tang (15.5%), respectively. Conclusion TCM use was associated with higher survival in patients with gastric cancer after surgery and adjuvant chemotherapy. TCM could be used as a complementary and alternative therapy in patients with gastric cancer after surgery and adjuvant chemotherapy.

Genetically Predicted C-Reactive Protein Associated With Postmenopausal Breast Cancer Risk: Interrelation With Estrogen and Cancer Molecular Subtypes Using Mendelian Randomization

BackgroundImmune-related etiologic pathways that influence breast cancer risk are incompletely understood and may be confounded by lifestyles or reverse causality. Using a Mendelian randomization (MR) approach, we investigated the potential causal relationship between genetically elevated C-reactive protein (CRP) concentrations and primary invasive breast cancer risk in postmenopausal women.MethodsWe used individual-level data obtained from 10,179 women, including 537 who developed breast cancer, from the Women’s Health Initiative Database for Genotypes and Phenotypes Study, which consists of five genome-wide association (GWA) studies. We examined 61 GWA single-nucleotide polymorphisms (SNPs) previously associated with CRP. We employed weighted/penalized weighted–medians and MR gene–environment interactions that allow instruments’ invalidity to some extent and attenuate the heterogeneous estimates of outlying SNPs.ResultsIn lifestyle-stratification analyses, genetically elevated CRP decreased risk for breast cancer in exogenous estrogen-only, estrogen + progestin, and past oral contraceptive (OC) users, but only among relatively short-term users (<5 years). Estrogen-only users for ≥5 years had more profound CRP-decreased breast cancer risk in dose–response fashion, whereas past OC users for ≥5 years had CRP-increased cancer risk. Also, genetically predicted CRP was strongly associated with increased risk for hormone-receptor positive or human epidermal growth factor receptor-2 negative breast cancer.ConclusionsOur findings may provide novel evidence on the immune-related molecular pathways linking to breast cancer risk and suggest potential clinical use of CRP to predict the specific cancer subtypes. Our findings suggest potential interventions targeting CRP–inflammatory markers to reduce breast cancer risk.

Nudging users towards better security decisions in password creation using whitebox-based multidimensional visualisations

ABSTRACT Nudging users to keep them secure online has become a growing research field in cybersecurity. While existing approaches are mainly blackbox based, showing aggregated visualisations as one-size-fits-all nudges, personalisation turned out promising to enhance the efficacy of nudges within the high variance of users and contexts. This article presents a disaggregated whitebox-based visualisation of critical information as a novel nudge. By segmenting users according to their decision-making and information processing styles, we investigate if the novel nudge is more effective for specific users than a common black-box nudge. Based on existing literature about critical factors in password security, we designed a dynamic radar chart and parallel coordinates as disaggregated visualisations. We evaluated the short-term effectiveness and users' perception of the nudges in a think-aloud prestudy and a representative online evaluation (N=1.012). Our findings suggest that dynamic radar charts present a moderately effective nudge towards stronger passwords regarding short-term efficacy and are appreciated particularly by players of role-playing games.