Testing quantum correlations in a confined atomic cloud by scattering fast atoms

We suggest measuring one-particle density matrix of a trapped ultracold atomic cloud by scattering fast atoms in a pure momentum state off the cloud. The lowest-order probability of the inelastic process, resulting in a pair of outcoming fast atoms for each incoming one, turns out to be given by a Fourier transform of the density matrix. Accordingly, important information about quantum correlations can be deduced directly from the differential scattering cross-section. A possible design of the atomic detector is also discussed.Comment: 5 RevTex pages, no figures, submitted to PR

Prediction of type 1 diabetes using a genetic risk model in the Diabetes Autoimmunity Study in the Young.

Background: Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects. Methods: The 10-factor weighted risk model (HLA, PTPN22, INS, IL2RA, ERBB3, ORMDL3, BACH2, IL27, GLIS3, RNLS), 3-factor model (HLA, PTPN22, INS), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941). Results: Stratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P=.00006; FDR: P=.0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P=.03; FDR, P=.01). In GP, the restricted 3-factor model was superior to HLA (P=.03), but not different from the 10-factor model (P=.22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P=.12) and performed worse than the 10-factor model (P=.02) Conclusions: We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups

A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk

OBJECTIVE: We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals. RESEARCH DESIGN AND METHODS: We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients’ relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2–51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial–Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables. RESULTS: Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06–1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47–3.51; P = 0.0002). CONCLUSIONS: The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.Maria J. Redondo, Susan Geyer, Andrea K. Steck, Seth Sharp, John M. Wentworth, Michael N. Weedon, Peter Antinozzi, Jay Sosenko, Mark Atkinson, Alberto Pugliese, Richard A. Oram, and the Type, Diabetes TrialNet Study Group (Frost, J. Amrhein, ... J. Couper ... et al.

Predictors of progression from the appearance of islet autoantibodies to early childhood diabetes: The Environmental Determinants of Diabetes in the Young (TEDDY).

OBJECTIVE: While it is known that there is progression to diabetes in <10 years in 70% of children with two or more islet autoantibodies, predictors of the progression to diabetes are only partially defined. RESEARCH DESIGN AND METHODS: The Environmental Determinants of Diabetes in the Young (TEDDY) study has observed 8,503 children who were at increased genetic risk for autoimmune diabetes. Insulin autoantibodies (IAAs), GAD65 autoantibodies (GADAs), and insulinoma-associated protein 2 autoantibodies (IA-2As) were measured every 3 months until 4 years of age and every 6 months thereafter; if results were positive, the autoantibodies were measured every 3 months. RESULTS: Life table analysis revealed that the cumulative incidence of diabetes by 5 years since the appearance of the first autoantibody differed significantly by the number of positive autoantibodies (47%, 36%, and 11%, respectively, in those with three autoantibodies, two autoantibodies, and one autoantibody, P < 0.001). In time-varying survival models adjusted for first-degree relative status, number of autoantibodies, age at first persistent confirmed autoantibodies, and HLA genotypes, higher mean IAA and IA-2A levels were associated with an increased risk of type 1 diabetes in children who were persistently autoantibody positive (IAAs: hazard ratio [HR] 8.1 [95% CI 4.6-14.2]; IA-2A: HR 7.4 [95% CI 4.3-12.6]; P < 0.0001]. The mean GADA level did not significantly affect the risk of diabetes. CONCLUSIONS: In the TEDDY study, children who have progressed to diabetes usually expressed two or more autoantibodies. Higher IAA and IA-2A levels, but not GADA levels, increased the risk of diabetes in those children who were persistently autoantibody positive

Residual beta-cell function in diabetes children followed and diagnosed in the TEDDY study compared to community controls.

ObjectiveTo explore whether children diagnosed with type 1 diabetes during islet autoantibody surveillance through The Environmental Determinants of Diabetes in the Young (TEDDY) study retain greater islet function than children diagnosed through the community. Methods TEDDY children identified at birth with high-risk human leukocyte antigen and followed every 3months until diabetes diagnosis were compared to age-matched children diagnosed with diabetes in the community. Both participated in long-term follow up after diagnosis. Hemoglobin A1c (HbA1c) and mixed meal tolerance test were performed within 1month of diabetes onset, then at 3, 6, and 12months, and biannually thereafter. ResultsComparison of 43 TEDDY and 43 paired control children showed that TEDDY children often had no symptoms (58%) at diagnosis and none had diabetic ketoacidosis (DKA) compared with 98% with diabetes symptoms and 14% DKA in the controls (P<0.001 and P=0.03, respectively). At diagnosis, mean HbA1c was lower in TEDDY (6.8%, 51mmol/mol) than control (10.5%, 91mmol/mol) children (P<0.0001). TEDDY children had significantly higher area under the curve and peak C-peptide values than the community controls throughout the first year postdiagnosis. Total insulin dose and insulin dose-adjusted A1c were lower throughout the first year postdiagnosis for TEDDY compared with control children. ConclusionsHigher C-peptide levels in TEDDY vs community-diagnosed children persist for at least 12months following diabetes onset and appear to represent a shift in the disease process of about 6months. Symptom-free diagnosis, reduction of DKA, and the potential for immune intervention with increased baseline C-peptide may portend additional long-term benefits of early diagnosis

Family adjustment to diabetes diagnosis in children: Can participation in a study on type 1 diabetes genetic risk be helpful?

BACKGROUND: Diagnosis of type 1 diabetes often causes a negative psychological impact on families. We examined whether parents and children enrolled in The Environmental Determinants of Diabetes in the Young (TEDDY) study differ in their psychological adjustment to diabetes diagnosis compared to children diagnosed with diabetes in the community. METHODS: TEDDY follows 8676 children at genetic risk for type 1 diabetes from birth. Fifty-four TEDDY children diagnosed with diabetes and 54 age-matched community control children diagnosed with diabetes were enrolled. Participants were aged 3 to 10 years and study visits occurred at 3, 6, and 12 months postdiagnosis. Psychological measures included an adapted diabetes-specific State Anxiety Inventory, the Pediatric Quality of Life Inventory-Diabetes Module, and the Pediatric Inventory for Parents, which measures frequency and difficulty of parenting stress. RESULTS: A generalized estimating equation analysis based on a difference score between TEDDY children and community controls found no significant differences between TEDDY parents and community controls on parent diabetes-specific anxiety (P = .30). However, TEDDY children exhibited better diabetes-specific quality of life (P = .03) and TEDDY parents reported lower frequency (P = .004) and difficulty (P = .008) of parenting stress compared to community controls. CONCLUSIONS: Children diagnosed with at-risk for type 1 diabetes who have previously enrolled in research monitoring have improved diabetes quality of life and lower parenting stress postdiagnosis compared to children diagnosed in the community. Families in follow-up studies may be more prepared if their child is diagnosed with diabetes

Progression from islet autoimmunity to clinical type 1 diabetes is influenced by genetic factors: Results from the prospective TEDDY study.

Background Progression time from islet autoimmunity to clinical type 1 diabetes is highly variable and the extent that genetic factors contribute is unknown.Methods In 341 islet autoantibody-positive children with the human leucocyte antigen (HLA) DR3/DR4-DQ8 or the HLA DR4-DQ8/DR4-DQ8 genotype from the prospective TEDDY (The Environmental Determinants of Diabetes in the Young) study, we investigated whether a genetic risk score that had previously been shown to predict islet autoimmunity is also associated with disease progression.Results Islet autoantibody-positive children with a genetic risk score in the lowest quartile had a slower progression from single to multiple autoantibodies (p=0.018), from single autoantibodies to diabetes (p=0.004), and by trend from multiple islet autoantibodies to diabetes (p=0.06). In a Cox proportional hazards analysis, faster progression was associated with an increased genetic risk score independently of HLA genotype (HR for progression from multiple autoantibodies to type 1 diabetes, 1.27, 95% CI 1.02 to 1.58 per unit increase), an earlier age of islet autoantibody development (HR, 0.68, 95% CI 0.58 to 0.81 per year increase in age) and female sex (HR, 1.94, 95% CI 1.28 to 2.93).Conclusions Genetic risk scores may be used to identify islet autoantibody-positive children with high-risk HLA genotypes who have a slow rate of progression to subsequent stages of autoimmunity and type 1 diabetes

An expression atlas of variant ionotropic glutamate receptors identifies a molecular basis of carbonation sensing

Through analysis of the Drosophila ionotropic receptors (IRs), a family of variant ionotropic glutamate receptors, we reveal that most IRs are expressed in peripheral neuron populations in diverse gustatory organs in larvae and adults. We characterise IR56d, which defines two anatomically-distinct neuron classes in the proboscis: one responds to carbonated solutions and fatty acids while the other represents a subset of sugar- and fatty acid-sensing cells. Mutational analysis indicates that IR56d, together with the broadly-expressed co-receptors IR25a and IR76b, is essential for physiological responses to carbonation and fatty acids, but not sugars. We further demonstrate that carbonation and fatty acids both promote IR56d-dependent attraction of flies, but through different behavioural outputs. Our work provides a toolkit for investigating taste functions of IRs, defines a subset of these receptors required for carbonation sensing, and illustrates how the gustatory system uses combinatorial expression of sensory molecules in distinct neurons to coordinate behaviour