Guest Editors’ Introduction On Understanding Ethical Behavior and Decision Making

Behavioral ethics is an emerging field that takes an empirical, social scientific approach to the study of business ethics. In this special issue, we include six articles that fall within the domain of behavioral ethics and that focus on three themes—moral awareness, ethical decision making, and reactions to unethical behavior. Each of the articles sheds additional light on the specific issues addressed. However, we hope this special issue will have an impact beyond that of the new insights offered in these articles, by stimulating evenmore research in this burgeoning field

Metal-insulator transition in a weakly interacting many-electron system with localized single-particle states

We consider low-temperature behavior of weakly interacting electrons in disordered conductors in the regime when all single-particle eigenstates are localized by the quenched disorder. We prove that in the absence of coupling of the electrons to any external bath dc electrical conductivity exactly vanishes as long as the temperatute $T$ does not exceed some finite value $T_c$. At the same time, it can be also proven that at high enough $T$ the conductivity is finite. These two statements imply that the system undergoes a finite temperature Metal-to-Insulator transition, which can be viewed as Anderson-like localization of many-body wave functions in the Fock space. Metallic and insulating states are not different from each other by any spatial or discrete symmetries. We formulate the effective Hamiltonian description of the system at low energies (of the order of the level spacing in the single-particle localization volume). In the metallic phase quantum Boltzmann equation is valid, allowing to find the kinetic coefficients. In the insulating phase, $T<T_c$, we use Feynmann diagram technique to determine the probability distribution function for quantum-mechanical transition rates. The probability of an escape rate from a given quantum state to be finite turns out to vanish in every order of the perturbation theory in electron-electron interaction. Thus, electron-electron interaction alone is unable to cause the relaxation and establish the thermal equilibrium. As soon as some weak coupling to a bath is turned on, conductivity becomes finite even in the insulating phase

Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c

Background/Objective: To identify and characterize subgroups of adolescents with type 1 diabetes (T1D) and elevated hemoglobin A1c (HbA1c) who share patterns in their continuous glucose monitoring (CGM) data as “dysglycemia phenotypes.”. Methods: Data were analyzed from the Flexible Lifestyles Empowering Change randomized trial. Adolescents with T1D (13-16 years, duration >1 year) and HbA1c 8% to 13% (64-119 mmol/mol) wore blinded CGM at baseline for 7 days. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18 months changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]). Results: The study sample included 234 adolescents (49.8% female, baseline age 14.8 ± 1.1 years, baseline T1D duration 6.4 ± 3.7 years, baseline HbA1c 9.6% ± 1.2%, [81 ± 13 mmol/mol]). Three Dysglycemia Clusters were identified with significant differences across all CGM metrics (P <.001). Dysglycemia Cluster 3 (n = 40, 17.1%) showed severe hypoglycemia and glycemic variability with moderate hyperglycemia and had a lower baseline HbA1c than Clusters 1 and 2 (P <.001). This cluster showed increases in HbA1c over 18 months (p-for-interaction = 0.006). No other baseline characteristics were associated with Dysglycemia Clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose monitoring, lower motivation, and lower adherence to diabetes self-management (all P <.05). Conclusions: There are subgroups of adolescents with T1D for which glycemic control is challenged by different aspects of dysglycemia. Enhanced understanding of demographic, behavioral, and clinical characteristics that contribute to CGM-derived dysglycemia phenotypes may reveal strategies to improve treatment

Metal enrichment processes

There are many processes that can transport gas from the galaxies to their environment and enrich the environment in this way with metals. These metal enrichment processes have a large influence on the evolution of both the galaxies and their environment. Various processes can contribute to the gas transfer: ram-pressure stripping, galactic winds, AGN outflows, galaxy-galaxy interactions and others. We review their observational evidence, corresponding simulations, their efficiencies, and their time scales as far as they are known to date. It seems that all processes can contribute to the enrichment. There is not a single process that always dominates the enrichment, because the efficiencies of the processes vary strongly with galaxy and environmental properties.Comment: 18 pages, 8 figures, accepted for publication in Space Science Reviews, special issue "Clusters of galaxies: beyond the thermal view", Editor J.S. Kaastra, Chapter 17; work done by an international team at the International Space Science Institute (ISSI), Bern, organised by J.S. Kaastra, A.M. Bykov, S. Schindler & J.A.M. Bleeke

Neutralino Dark Matter from MSSM Flat Directions in light of WMAP Result

The minimal supersymmetric standard model (MSSM) has a truly supersymmetric way to explain both the baryon asymmetry and cold dark matter in the present Universe, that is, ``Affleck-Dine baryo/DM-genesis.'' The associated late-time decay of Q-balls directly connects the origins of the baryon asymmetry and dark matter, and also predicts a specific nature of the LSP. In this paper, we investigate the prospects for indirect detection of these dark matter candidates observing high energy neutrino flux from the Sun, and hard positron flux from the halo. We also update the previous analysis of the direct detection in hep-ph/0205044 by implementing the recent result from WMAP satellite.Comment: 32 pages, including 40 figure

Dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes

Aims To determine the association between dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes. Methods Type 1 adolescents from a randomized trial wore a blinded continuous glucose monitoring (CGM) system at baseline for one week in free-living conditions. Dietary intake was calculated as the average from two 24-h dietary recalls. Non-severe hypoglycemia was defined as having blood glucose < 70 mg/dL for ≥ 10 min but not requiring external assistance, categorized as daytime and nocturnal (11 PM–7AM). Data were analyzed using logistic regression models. Results Among 98 participants with 14,277 h of CGM data, 70 had daytime hypoglycemia, 66 had nocturnal hypoglycemia, 55 had both, and 17 had neither. Soluble fiber and protein intake were positively associated with both daytime and nocturnal hypoglycemia. Glycemic index, monounsaturated fat, and polyunsaturated fat were negatively associated with daytime hypoglycemia only. Adjusting for total daily insulin dose per kilogram eliminated all associations. Conclusions Dietary intake was differentially associated with daytime and nocturnal hypoglycemia. Over 80% of type 1 adolescents had hypoglycemia in a week, which may be attributed to the mismatch between optimal insulin dose needed for each meal and actually delivered insulin dose without considering quality of carbohydrate and nutrients beyond carbohydrate

Associations of Dietary Intake with the Intestinal Microbiota and Short-Chain Fatty Acids Among Young Adults with Type 1 Diabetes and Overweight or Obesity

Background: Diet, a key component of type 1 diabetes (T1D) management, modulates the intestinal microbiota and its metabolically active byproducts—including SCFA—through fermentation of dietary carbohydrates such as fiber. However, the diet–microbiome relationship remains largely unexplored in longstanding T1D. Objectives: We evaluated whether increased carbohydrate intake, including fiber, is associated with increased SCFA-producing gut microbes, SCFA, and intestinal microbial diversity among young adults with longstanding T1D and overweight or obesity. Methods: Young adult men and women with T1D for ≥1 y, aged 19–30 y, and BMI of 27.0–39.9 kg/m2 at baseline provided stool samples at baseline and 3, 6, and 9 mo of a randomized dietary weight loss trial. Diet was assessed by 1–2 24-h recalls. The abundance of SCFA-producing microbes was measured using 16S rRNA gene sequencing. GC-MS measured fecal SCFA (acetate, butyrate, propionate, and total) concentrations. Adjusted and Bonferroni-corrected generalized estimating equations modeled associations of dietary fiber (total, soluble, and pectins) and carbohydrate (available carbohydrate, and fructose) with microbiome-related outcomes. Primary analyses were restricted to data collected before COVID-19 interruptions. Results: Fiber (total and soluble) and carbohydrates (available and fructose) were positively associated with total SCFA and acetate concentrations (n = 40 participants, 52 visits). Each 10 g/d of total and soluble fiber intake was associated with an additional 8.8 μmol/g (95% CI: 4.5, 12.8 μmol/g; P = 0.006) and 24.0 μmol/g (95% CI: 12.9, 35.1 μmol/g; P = 0.003) of fecal acetate, respectively. Available carbohydrate intake was positively associated with SCFA producers Roseburia and Ruminococcus gnavus. All diet variables except pectin were inversely associated with normalized abundance of Bacteroides and Alistipes. Fructose was inversely associated with Akkermansia abundance. Conclusions: In young adults with longstanding T1D, fiber and carbohydrate intake were associated positively with fecal SCFA but had variable associations with SCFA-producing gut microbes. Controlled feeding studies should determine whether gut microbes and SCFA can be directly manipulated in T1D

Prevalence and Predictors of Substance Use Disorders Among HIV Care Enrollees in the United States

Prior efforts to estimate U.S. prevalence of substance use disorders (SUDs) in HIV care have been undermined by caveats common to single-site trials. The current work reports on a cohort of 10,652 HIV-positive adults linked to care at seven sites, with available patient data including geography, demography, and risk factor indices, and with substance-specific SUDs identified via self-report instruments with validated diagnostic thresholds. Generalized estimating equations also tested patient indices as SUD predictors. Findings were: (1) a 48 % SUD prevalence rate (between-site range of 21–71 %), with 20 % of the sample evidencing polysubstance use disorder; (2) substance-specific SUD rates of 31 % for marijuana, 19 % alcohol, 13 % methamphetamine, 11 % cocaine, and 4 % opiate; and (3) emergence of younger age and male gender as robust SUD predictors. Findings suggest high rates at which SUDs occur among patients at these urban HIV care sites, detail substance-specific SUD rates, and identify at-risk patient subgroups

Identifying HIV care enrollees at-risk for cannabis use disorder

Increased scientific attention given to cannabis in the United States has particular relevance for its domestic HIV care population, given that evidence exists for both cannabis as a therapeutic agent and cannabis use disorder (CUD) as a barrier to antiretroviral medication adherence. It is critical to identify relative risk for CUD among demographic subgroups of HIV patients, as this will inform detection and intervention efforts. A Center For AIDS Research Network of Integrated Clinical Systems cohort (N = 10,652) of HIV-positive adults linked to care at seven United State sites was examined for this purpose. Based on a patient-report instrument with validated diagnostic threshold for CUD, the prevalence of recent cannabis use and corresponding conditional probabilities for CUD were calculated for the aggregate sample and demographic subgroups. Generalized estimating equations then tested models directly examining patient demographic indices as predictors of CUD, while controlling for history and geography. Conditional probability of CUD among cannabis-using patients was 49%, with the highest conditional probabilities among demographic subgroups of young adults and those with non-specified sexual orientation (67–69%) and the lowest conditional probability among females and those 50+ years of age (42% apiece). Similarly, youthful age and male gender emerged as robust multivariate model predictors of CUD. In the context of increasingly lenient policies for use of cannabis as a therapeutic agent for chronic conditions like HIV/AIDS, current study findings offer needed direction in terms of specifying targeted patient groups in HIV care on whom resources for enhanced surveillance and intervention efforts will be most impactful

Influence of Substance Use Disorders on 2-Year HIV Care Retention in the United States

Substance use disorders (SUDs) are thought to predict care discontinuity, though magnitude and substance-specific variance of effects are unclear. This report of analytic work undertaken with a multi-regional American cohort of 9153 care enrollees addresses these gaps. Care retention was computed from 24-month post-linkage clinic visit documentation, with SUD cases identified from patient-report screening instruments. Two generalized estimating equations tested binary and hierarchial SUD predictors of retention, and potential effect modification by patient age-group, sex, and care site. Findings demonstrate: (1) detrimental SUD effect, equivalent to a nine percentage-point decrease in retention, with independent effects of age-group and care site; (2) substance-specific effect of marijuana UD associated with lower retention; and (3) age-modification of each effect on care discontinuity, with SUDs serving as a risk factor among 18–29 year-olds and protective factor among 60+ year-olds. Collective findings document patient attributes as influences that place particular subgroups at-risk to discontinue care