Making Informed Choices On Incorporating Chemoprevention into carE (MiCHOICE, SWOG 1904): Design and methods of a cluster randomized controlled trial.

INTRODUCTION: Women with atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS) have a significantly increased risk of breast cancer, which can be substantially reduced with antiestrogen therapy for chemoprevention. However, antiestrogen therapy for breast cancer risk reduction remains underutilized. Improving knowledge about breast cancer risk and chemoprevention among high-risk patients and their healthcare providers may enhance informed decision-making about this critical breast cancer risk reduction strategy. METHODS/DESIGN: We are conducting a cluster randomized controlled trial to evaluate the effectiveness and implementation of patient and provider decision support tools to improve informed choice about chemoprevention among women with AH or LCIS. We have cluster randomized 26 sites across the U.S. through the SWOG Cancer Research Network. A total of 415 patients and 200 healthcare providers are being recruited. They are assigned to standard educational materials alone or combined with the web-based decision support tools. Patient-reported and clinical outcomes are assessed at baseline, after a follow-up visit at 6 months, and yearly for 5 years. The primary outcome is chemoprevention informed choice after the follow-up visit. Secondary endpoints include other patient-reported outcomes, such as chemoprevention knowledge, decision conflict and regret, and self-reported chemoprevention usage. Barriers and facilitators to implementing decision support into clinic workflow are assessed through patient and provider interviews at baseline and mid-implementation. RESULTS/DISCUSSION: With this hybrid effectiveness/implementation study, we seek to evaluate if a multi-level intervention effectively promotes informed decision-making about chemoprevention and provide valuable insights on how the intervention is implemented in U.S. TRIAL REGISTRATION: NCT04496739.

The Simons Observatory: Cryogenic half wave plate rotation mechanism for the small aperture telescopes.

We present the requirements, design, and evaluation of the cryogenic continuously rotating half-wave plate (CHWP) for the Simons Observatory (SO). SO is a cosmic microwave background polarization experiment at Parque Astronómico de Atacama in northern Chile that covers a wide range of angular scales using both small (⌀0.42 m) and large (⌀6 m) aperture telescopes. In particular, the small aperture telescopes (SATs) focus on large angular scales for primordial B-mode polarization. To this end, the SATs employ a CHWP to modulate the polarization of the incident light at 8 Hz, suppressing atmospheric 1/f noise and mitigating systematic uncertainties that would otherwise arise due to the differential response of detectors sensitive to orthogonal polarizations. The CHWP consists of a 505 mm diameter achromatic sapphire HWP and a cryogenic rotation mechanism, both of which are cooled down to ∼50 K to reduce detector thermal loading. Under normal operation, the HWP is suspended by a superconducting magnetic bearing and rotates with a constant 2 Hz frequency, controlled by an electromagnetic synchronous motor. We find that the number of superconductors and the number of magnets that make up the superconducting magnetic bearing are important design parameters, especially for the rotation mechanism's vibration performance. The rotation angle is detected through an angular encoder with a noise level of 0.07 µrad s. During a cooldown process, the rotor is held in place by a grip-and-release mechanism that serves as both an alignment device and a thermal path. In this paper, we provide an overview of the SO SAT CHWP: its requirements, hardware design, and laboratory performance.

James H. (Jim) Sidanius (1945-2021).

Memorializes Jim Sidanius (née James Brown [1945-2021]), one of the the foremost social and political psychologists of his generation. His theory of social dominance redefined the scientific study of intergroup relations, advancing novel hypotheses regarding the causes and consequences of intergroup conflict and inequality by integrating insights across the social and biological sciences. Jim's theoretical insights were matched only by his empirical prowess; he was a master at analyzing large data sets with advanced statistical methods, methods that he taught to hundreds of doctoral students over the years in his notoriously challenging but rewarding graduate statistics courses at the University of California, Los Angeles (UCLA), and Harvard. Beyond his teaching of statistics and advanced topics in social psychology and African American studies, Jim mentored dozens of aspiring intergroup relations scholars over a 44-year career. As one of few Black social psychologists, he served as a role model for young Black scholars in particular. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.

MOTIVATION: During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other. Moreover, they are inadequate to elucidate why compounds participate in specific pathways. RESULTS: To address these issues, we propose a novel Multi-Label Graph Learning framework of Metabolic Pathway prediction boosted by pathway interdependence, called MLGL-MP, which contains a compound encoder, a pathway encoder and a multi-label predictor. The compound encoder learns compound embedding representations by graph neural networks. After constructing a pathway dependence graph by re-trained word embeddings and pathway co-occurrences, the pathway encoder learns pathway embeddings by graph convolutional networks. Moreover, after adapting the compound embedding space into the pathway embedding space, the multi-label predictor measures the proximity of two spaces to discriminate which pathways a compound participates in. The comparison with state-of-the-art methods on KEGG pathways demonstrates the superiority of our MLGL-MP. Also, the ablation studies reveal how its three components contribute to the model, including the pathway dependence, the adapter between compound embeddings and pathway embeddings, as well as the pre-training strategy. Furthermore, a case study illustrates the interpretability of MLGL-MP by indicating crucial substructures in a compound, which are significantly associated with the attending metabolic pathways. It is anticipated that this work can boost metabolic pathway predictions in drug discovery. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are freely available at https://github.com/dubingxue/MLGL-MP.

Drug-drug interaction prediction with learnable size-adaptive molecular substructures.

Drug-drug interactions (DDIs) are interactions with adverse effects on the body, manifested when two or more incompatible drugs are taken together. They can be caused by the chemical compositions of the drugs involved. We introduce gated message passing neural network (GMPNN), a message passing neural network which learns chemical substructures with different sizes and shapes from the molecular graph representations of drugs for DDI prediction between a pair of drugs. In GMPNN, edges are considered as gates which control the flow of message passing, and therefore delimiting the substructures in a learnable way. The final DDI prediction between a drug pair is based on the interactions between pairs of their (learned) substructures, each pair weighted by a relevance score to the final DDI prediction output. Our proposed method GMPNN-CS (i.e. GMPNN + prediction module) is evaluated on two real-world datasets, with competitive results on one, and improved performance on the other compared with previous methods. Source code is freely available at https://github.com/kanz76/GMPNN-CS.

Beyond Black and White: Conceptualizing and essentializing Black-White identity.

OBJECTIVE: Psychological research suggests that Black-White individuals are often conceptualized as Black and White, and that essentialist beliefs about race are negatively associated with conceptualizing Black-White individuals as such. The present research examined what people think it means to be Black and White (e.g., a mixture of Black and White vs. completely Black and completely White) and whether essentialism is indeed negatively associated with such concepts. METHOD: We used multiple methodologies (e.g., surveys, open-ended explanations, experimental manipulations) to examine how Black, White, and Black-White perceivers conceptualized Black-White individuals (Studies 1-3) and the extent to which essentialist beliefs, both dispositional (Studies 2-3) and experimentally induced (Study 4), predicted those concepts. RESULTS: We find that U.S. Black-White individuals most often conceptualized "Black and White" to mean a mixture of Black and White (Study 1), as did U.S. White individuals and U.S. Black individuals (Studies 2 and 3), and that racial essentialism-both dispositional (Studies 2 and 3) and experimentally manipulated (Study 4)-was positively associated with this conception. CONCLUSION: Our data shed new light on the complexity of race concepts and essentialism and advance the psychological understanding of Black-White identity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The odd log-logistic generalized exponential distribution: Application on survival times of chemotherapy patients data.

Background: The creation of developing new generalized classes of distributions has attracted applied and theoretical statisticians owing to their properties of flexibility. The development of generalized distribution aims to find distribution flexibility and suitability for available data. In this decade, most authors have developed classes of distributions that are new, to become valuable for applied researchers. Methods: This study aims to develop the odd log-logistic generalized exponential distribution (OLLGED), one of the lifetime newly generated distributions in the field of statistics. The advantage of the newly generated distribution is the heavily tailed distributed lifetime data set. Most of the probabilistic properties are derived including generating functions, moments, and quantile and order statistics. Results: Estimation of the model parameter is done by the maximum likelihood method. The performance of parametric estimation is studied through simulation. Application of OLLGED and its flexibilities is done using two data sets and while its performance is done on the randomly simulated data set. Conclusions: The application and flexibility of the OLLGED are ensured through empirical observation using two sets of lifetime data, establishing that the proposed OLLGED can provide a better fit in comparison to existing rival models, such as odd generalized log-logistic, type-II generalized log-logistic, exponential distributions, odd exponential log-logistic, generalized exponential, and log-logistic.

Phytocenosis biodiversity at various water levels in mesotrophic Lake Arakhley, Lake Baikal basin, Russia.

Small lakes have lower water levels during dry years as was the case in 2000-2020. We sought to show the biodiversity of plant communities at various water levels in Lake Arakhley. Changes in moisture content are reflected in the cyclical variations of the water level in the lake, which decreased approximately 2 m in 2017-2018. These variations affect the biological diversity of the aquatic ecosystems. We present the latest data on the state of the plant communities in this mesotrophic lake located in the drainage basin of Lake Baikal. Lake Arakhley is a freshwater lake with low mineral content and a sodium hydrocarbonate chemical composition. Changes in the nutrient concentration were related to precipitation; inflow volume and organic matter were autochtonous at low water levels. The most diverse groups of phytoplankton found in the lake were Bacillariophyta, Chlorophyta, and Chrysophyta. High biodiversity values indicate the complexity and richness of the lake's phytoplankton community. A prevalence of Lindavia comta was observed when water levels were low and Asterionella formosa dominated in high-water years. The maximum growth depth of lacustrine vegetation decreased from 11.0 m to 4.0 m from 1967 to 2018. Decreasing water levels were accompanied by a reduction in the littoral zone, altering the communities of aquatic plants. The hydrophyte communities were monodominant in the dry years and were represented by Ceratophyllum demersum. The vegetation cover of the lake was more diverse in high-water years and variations in the lake's water content altered the composition of biogenic substances. These changes were reflected in the lake's phytocenosis.

SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism. Although several computational methods have been proposed for identifying potential adverse DDIs, there is still room for improvement. Existing methods are not explicitly based on the knowledge that DDIs are fundamentally caused by chemical substructure interactions instead of whole drugs' chemical structures. Furthermore, most of existing methods rely on manually engineered molecular representation, which is limited by the domain expert's knowledge.We propose substructure-substructure interaction-drug-drug interaction (SSI-DDI), a deep learning framework, which operates directly on the raw molecular graph representations of drugs for richer feature extraction; and, most importantly, breaks the DDI prediction task between two drugs down to identifying pairwise interactions between their respective substructures. SSI-DDI is evaluated on real-world data and improves DDI prediction performance compared to state-of-the-art methods. Source code is freely available at https://github.com/kanz76/SSI-DDI.

Ideology selectively shapes attention to inequality.

Contemporary debates about addressing inequality require a common, accurate understanding of the scope of the issue at hand. Yet little is known about who notices inequality in the world around them and when. Across five studies (N = 8,779) employing various paradigms, we consider the role of ideological beliefs about the desirability of social equality in shaping individuals' attention to-and accuracy in detecting-inequality across the class, gender, and racial domains. In Study 1, individuals higher (versus lower) on social egalitarianism were more likely to naturalistically remark on inequality when shown photographs of urban scenes. In Study 2, social egalitarians were more accurate at differentiating between equal versus unequal distributions of resources between men and women on a basic cognitive task. In Study 3, social egalitarians were faster to notice inequality-relevant changes in images in a change detection paradigm indexing basic attentional processes. In Studies 4 and 5, we varied whether unequal treatment adversely affected groups at the top or bottom of society. In Study 4, social egalitarians were, on an incentivized task, more accurate at detecting inequality in speaking time in a panel discussion that disadvantaged women but not when inequality disadvantaged men. In Study 5, social egalitarians were more likely to naturalistically point out bias in a pattern detection hiring task when the employer was biased against minorities but not when majority group members faced equivalent bias. Our results reveal the nuances in how our ideological beliefs shape whether we accurately notice inequality, with implications for prospects for addressing it.

Should Individuals Think Like Their Group? A Descriptive-to-Prescriptive Tendency Toward Group-Based Beliefs.

Across three pre-registered studies with children (ages 4-9) and adults (N = 303), we examined whether how a group is predicted evaluations of how group members should be (i.e., a descriptive-to-prescriptive tendency), under conditions in which the descriptive group norms entailed beliefs that were fact-based (Study 1), opinion-based (Study 2), and ideology-based (Study 3). Overall, participants tended to disapprove of individuals with beliefs that differed from their group, but the extent of this tendency varied across development and as a function of the belief under consideration (e.g., younger children did not show a descriptive-to-prescriptive tendency in the context of facts and ideologies, suggesting that they prioritized truth over group norms). Implications for normative reasoning and ideological polarization are discussed.

A cryogenic continuously rotating half-wave plate mechanism for the POLARBEAR-2b cosmic microwave background receiver.

We present the design and laboratory evaluation of a cryogenic continuously rotating half-wave plate (CHWP) for the POLARBEAR-2b (PB-2b) cosmic microwave background receiver, the second installment of the Simons Array. PB-2b will observe at 5200 m elevation in the Atacama Desert of Chile in two frequency bands centered at 90 GHz and 150 GHz. In order to suppress atmospheric 1/f noise and mitigate systematic effects that arise when differencing orthogonal detectors, PB-2b modulates linear sky polarization using a CHWP rotating at 2 Hz. The CHWP has a 440 mm clear aperture diameter and is cooled to ≈50 K in the PB-2b receiver cryostat. It consists of a low-friction superconducting magnetic bearing and a low-torque synchronous electromagnetic motor, which together dissipate <2 W. During cooldown, a grip-and-release mechanism centers the rotor to <0.5 mm, and during continuous rotation, an incremental optical encoder measures the rotor angle with a noise level of 0.1 µrad/Hz. We discuss the experimental requirements for the PB-2b CHWP, the designs of its various subsystems, and the results of its evaluation in the laboratory. The presented CHWP has been deployed to Chile and is expected to see first light on PB-2b in 2020 or 2021.

Digitizing clinical trials.

Clinical trials are a fundamental tool used to evaluate the efficacy and safety of new drugs and medical devices and other health system interventions. The traditional clinical trials system acts as a quality funnel for the development and implementation of new drugs, devices and health system interventions. The concept of a "digital clinical trial" involves leveraging digital technology to improve participant access, engagement, trial-related measurements, and/or interventions, enable concealed randomized intervention allocation, and has the potential to transform clinical trials and to lower their cost. In April 2019, the US National Institutes of Health (NIH) and the National Science Foundation (NSF) held a workshop bringing together experts in clinical trials, digital technology, and digital analytics to discuss strategies to implement the use of digital technologies in clinical trials while considering potential challenges. This position paper builds on this workshop to describe the current state of the art for digital clinical trials including (1) defining and outlining the composition and elements of digital trials; (2) describing recruitment and retention using digital technology; (3) outlining data collection elements including mobile health, wearable technologies, application programming interfaces (APIs), digital transmission of data, and consideration of regulatory oversight and guidance for data security, privacy, and remotely provided informed consent; (4) elucidating digital analytics and data science approaches leveraging artificial intelligence and machine learning algorithms; and (5) setting future priorities and strategies that should be addressed to successfully harness digital methods and the myriad benefits of such technologies for clinical research.

The role of group-based egalitarianism in collective action.

Whereas social dominance theory has historically been used to understand the dynamics of group-based hierarchy and oppression, it has seldom been used to understand the dynamics of social change toward greater equality. We review a growing body of research that takes seriously the psychology of individuals who are interested in group-based equality and hierarchy challenge - those lower (versus higher) in social dominance orientation (SDO). This emerging research documents that lower SDO individuals are more likely to support hierarchy-attenuating policies and collective action, and identifies underlying mechanisms (e.g. perceptions of injustice). Moreover, this research suggests that egalitarian ideology can help account for efforts to change the hierarchal status quo, even among high status group members who materially benefit from the extant hierarchy.

Theoretical Approaches and Practical Assessment of Socio-Economic Effects of Desertification in Mongolia.

In this paper, we consider the effects of desertification in Mongolia, where the area of degraded land has increased significantly in the recent decade. Currently, almost the entire territory of the country is subject to varying degrees of degradation. The intensity of the desertification processes in different natural zones is influenced by both natural climatic and anthropogenic factors. The purpose of this study is to evaluate the impact of desertification on environmental and socio-economic living conditions, as well as on living standards of the local population. In this work, for the first time, the socio-economic aspects of desertification have been studied on a common methodological basis in different Mongolian aimags over a ten-year period. In order to carry out in-depth research, we used the submeridional and sublatitudinal principles for selecting the model study areas, as well as specific criteria and expert assessment. We used a sociological survey as the main method, based on a designed questionnaire, which was translated into Mongolian. The questionnaire includes questions regarding the influence of desertification on traditional nomadic farming, health of family members, water supply of households, water quality, living standards, etc. The results of the sociological surveys made it possible to draw conclusions on the impact of desertification on households, to identify the main problems of local people, and to describe the dynamics of the socio-economic status of the population living in the model areas. Our studies have demonstrated the intensification of the impact of desertification processes in different natural zones, administrative-territorial units and settlement systems in Mongolia.

Catheter securement impact on PICC-related CLABSI: A university hospital perspective.

The use of a subcutaneous engineered securement device (SESD) for peripherally inserted central catheters (PICC) in an acute care setting was found to have a direct impact on central line associated bloodstream infection (CLABSI) rates compared to traditional adhesive engineered securement devices (AESD). OBJECTIVE: While the literature suggests the use of SESDs has had successful results for device securement, it is unknown to what extent they may impact CLABSI rates. Securement and stabilization performance among devices may be a direct risk factor for CLABSIs. METHODS: A retrospective quality review of 7,776 cases was conducted at a large academic medical center. The primary researcher implemented a quantitative design which was analyzed with demographics statistics and relative risk ratio. RESULTS: There was a 288% (n = 47) increase in relative risk of CLABSI found in the AESD group compared to the SESD group. The results imply the use of SESDs may improve nursing practice and patient outcomes lowering CLABSI rates in patients with PICCs by a reduction of risks associated with securement design differences.

Introducing the Sociopolitical Motive × Intergroup Threat Model to Understand How Monoracial Perceivers' Sociopolitical Motives Influence Their Categorization of Multiracial People.

Researchers have used social dominance, system justification, authoritarianism, and social identity theories to understand how monoracial perceivers' sociopolitical motives influence their categorization of multiracial people. The result has been a growing understanding of how particular sociopolitical motives and contexts affect categorization, without a unifying perspective to integrate these insights. We review evidence supporting each theory's predictions concerning how monoracial perceivers categorize multiracial people who combine their ingroup with an outgroup, with attention to the moderating role of perceiver group status. We find most studies cannot arbitrate between theories of categorization and reveal additional gaps in the literature. To advance this research area, we introduce the sociopolitical motive × intergroup threat model of racial categorization that (a) clarifies which sociopolitical motives interact with which intergroup threats to predict categorization and (b) highlights the role of perceiver group status. Furthermore, we consider how our model can help understand phenomena beyond multiracial categorization.

Precision Measurements of the

Measurement of the ^{138}Ba^{+} ^{2}S_{1/2}-^{2}D_{5/2} clock transition frequency and D_{5/2} Landé g_{J} factor are reported. The clock transition frequency ν_{Ba^{+}}=170126432449333.31±(0.39)_{stat}±(0.29)_{sys} Hz, is obtained with accuracy limited by the frequency calibration of the maser used as a reference oscillator. The Landé g_{J} factor for the ^{2}D_{5/2} level is determined to be g_{D}=1.20036739(24), which is a 30-fold improvement on previous measurements. The g-factor measurements are corrected for an ac-magnetic field from trap-drive-induced currents in the electrodes, and data taken over a range of magnetic fields underscores the importance of accounting for this systematic.

Internal Delensing of Cosmic Microwave Background Polarization B-Modes with the POLARBEAR Experiment.

Using only cosmic microwave background polarization data from the polarbear experiment, we measure B-mode polarization delensing on subdegree scales at more than 5σ significance. We achieve a 14% B-mode power variance reduction, the highest to date for internal delensing, and improve this result to 22% by applying for the first time an iterative maximum a posteriori delensing method. Our analysis demonstrates the capability of internal delensing as a means of improving constraints on inflationary models, paving the way for the optimal analysis of next-generation primordial B-mode experiments.

Hyperfine Averaging by Dynamic Decoupling in a Multi-Ion Lutetium Clock.

We propose and experimentally demonstrate a scheme that realizes hyperfine averaging during a Ramsey interrogation of a clock transition. The method eliminates the need to average over multiple optical transitions, reduces the sensitivity of the clock to its environment, and reduces inhomogeneous broadening in a multi-ion clock. The method is compatible with autobalanced Ramsey spectroscopy, which facilitates the elimination of residual shifts due to imperfect implementation and ac stark shifts from the optical probe. We demonstrate the scheme using correlation spectroscopy of the ^{1}S_{0}↔^{3}D_{1} clock transition in a three-ion Lu^{+} clock. From the demonstration we are able to provide a measurement of the ^{3}D_{1} quadrupole moment, Θ(^{3}D_{1})=0.634(9)ea_{0}^{2}.