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People commonly hypothesize that an independent variable follows a Gamma distribution in many areas,including hydrology,meteorology and insurance claim.Under the Gamma distribution assumption,Gamma regression model enables an outstanding fitting effect,compared with multivariate linear-regression model.Previous studies may be able to obtain a Gamma regression model trained only on a public dataset.However,when the datasets are provided by multiple parties,how to seek to address the problem of data privacy by training Gamma regression model without exchanging the data itself? A secure multi-party federated Gamma regression algorithm has been applied to this area.Firstly,the log-likelihood function is derived with the iterative method.Secondly,the link function is determined according to the fact,and the gradient updating strategy is constructed by the loss function.Finally,the parameters with homomorphic encryption are updated,then the training is completed.The model is tested on two public datasets,and the results show that under the premise of privacy protection our method can effectively use the value of multi-party data to generate Gamma regression model.The fitting performance of our method is better than that of Gamma regression model implements in a single part,and is close to the result yielded by centralized data learning model.

C24H22N8O4S2Co, orthorhombic, Aba2 (no. 41), a = 12.043(5) Å, b = 21.723(9) Å, c = 9.961(4) Å, V = 2605.9(18) Å3, Z = 4, Rgt(F) = 0.0366, wRref(F2) = 0.0764, T = 296(2) K.

C24H32CuN4O4, monoclinic, Cc, a = 11.8142(10) Å, b = 45.481(4) Å, c = 9.2540(8) Å, β = 97.886(2)°, V = 4925.4(8) Å3, Z = 8, Rgt(F) = 0.0501, wRref(F2) = 0.1188, T = 296(2) K.

C13H17N3OS, triclinic, P1̄ (no. 2), a = 6.9906(9) Å, b = 8.0075(11) Å, c = 16.057(2) Å, α = 81.822(2)°, β = 89.151(2)°, γ = 70.735(2)°, V = 839.4(2) Å3, Z = 2, Rgt(F) = 0.0444, wRref(F2) = 0.1299, T = 296(2) K.

C24H22N8O4S2Cu, orthorhombic, Aba2 (no. 41), a = 11.9395(18) Å, b = 21.813(3) Å, c = 10.1184(15) Å, V = 2635.2(7) Å3, Z = 4, Rgt(F) = 0.0395, wRref(F2) = 0.0912, T = 296(2) K.

C24H32CuN4O4, monoclinic, Cc, a = 11.8142(10) Å, b = 45.481(4) Å, c = 9.2540(8) Å, β = 97.886(2)°, V = 4925.4(8) Å3, Z = 8, Rgt(F) = 0.0501, wRref(F2) = 0.1188, T = 296(2) K. [ABSTRACT FROM AUTHOR]

Background: This study aimed to assess the relationship between dyslipidemia (DL) risk and health-related physical fitness (HPF) and evaluated the prognostic value of HPF for risk of DL. Methods: A total of 776 university staff members were recruited, of which 407 were females, and 369 males. Blood samples and HPF tests were collected from all participants after 12 h fasting. Results: The prevalence of DL was 41.77% and 51.49% in female and male university staff members, respectively, and there was no significant difference between genders (χ2 = 2.687, p = 0.101). According to the logistic regression analysis, age, male sex, GLU, hypertension, BMI, BF, WHtR, and LAP were significant risk factors for DL (p < 0.05), VCI and, SAR were significant protective factors for DL (p < 0.05), and SMI, GS, and VG were not significantly associated with the risk of DL. The area under the receiver-operating characteristic (ROC) curve (AUC) analysis indicated that, LAP (AUC: 0.730, 95CI%: 0.697–0.762), WHtR (AUC: 0.626, 95CI%: 0.590–0.660), and BMI (AUC: 0.599, 95CI%: 0.563–0.634) are valid predictors of DL, and LAP and WHtR perform better than BMI (Z = 8.074, p < 0.001) in predicting DL in male and female university staff members. Conclusion: The risk of DL is significantly related to body composition, cardiorespiratory fitness, and flexibility. LAP and WHtR perform better than BMI in predicting risk of DL in male and female university staff members. [ABSTRACT FROM AUTHOR]

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C38H36N4O6Cl2Cu, triclinic, P1̄ (no. 2), a = 10.749(5) Å, b = 11.074(5) Å, c = 15.951(7) Å, α = 80.471(8)°, β = 80.987(8)°, γ = 77.283(8)°, V = 1812.3(14) Å3, Z = 2, Rgt(F) = 0.0465, wRref(F2) = 0.1279, T = 296(2) K. [ABSTRACT FROM AUTHOR]

C24H22N8O4S2Cu, orthorhombic, Aba2 (no. 41), a = 11.9395(18) Å, b = 21.813(3) Å, c = 10.1184(15) Å, V = 2635.2(7) Å3, Z = 4, Rgt(F) = 0.0395, wRref(F2) = 0.0912, T = 296(2) K. [ABSTRACT FROM AUTHOR]

C16H17NO3, orthorhombic, Pna21 (no. 33), a = 10.3470(3) Å, b = 7.9347(2) Å, c = 17.0947(4) Å, V = 403.48(6) Å3, Z = 4, Rgt(F) = 0.0427, wRref(F2) = 0.1044, T = 296(2) K. [ABSTRACT FROM AUTHOR]

C16H17NO3, orthorhombic, Pna21 (no. 33), a = 10.3470(3) Å, b = 7.9347(2) Å, c = 17.0947(4) Å, V = 403.48(6) Å3, Z = 4, Rgt(F) = 0.0427, wRref(F2) = 0.1044, T = 296(2) K. [ABSTRACT FROM AUTHOR]

A simple chemosensor, 6-(1H-pyrrol-2-yl)-5,6-dihydro-benzo[4,5]imidazo[1,2-c]quinazoline (1), was synthesized via simple nucleophilic addition reaction coupled with Schiff base condensation. The probe 1 is aggregation-induced emission-active and could be used as an on-off fluorescence sensor toward Cu 2+ in H 2 O/CH 3 CN (99.5%, v/v) solution. Furthermore, the resultant Cu 2+ complex selectively responded to pyrophosphate (PPi) among various anions based on fluorescent on-off signal. In addition, the probe could be used for detecting Cu 2+ and PPi in HeLa cells., (Copyright © 2019 Elsevier B.V. All rights reserved.)