Modeling approaches to the indirect estimation of migration flows: From entropy to EM

The paper presents probability models to recover information on migration flows from incomplete data. Models are used to predict migration and to combine data from different sources. The parameters of the model are estimated from the data by the maximum likelihood method. If data are incomplete, an extension of the maximum likelihood method, the EM algorithm, may be applied. Two models are considered: the binomial (multinomial) model, which underlies the logit model and the logistic regression, and the Poisson model, which underlies the loglinear model, the log-rate model and the Poisson regression. The binomial model is viewed in relation to the Poisson model. By way of illustration, the probabilistic approach and the EM algorithm are applied to two different missing data problems. The first problem is the prediction of migration flows using spatial interaction models. The probabilistic approach is compared to conventional methods, such as the gravity model and entropy maximization. In fact, spatial interaction models are particular variants of log-linear models. The second problem is one of unobserved heterogeneity. A mixture model is applied to determine the relative sizes of different migrant categories.Migration, Missing data, Probability models, Entropy, Maximum likelihood, EM,

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

Construction support also was granted by the United States National Science Foundation under Award PHY-0619727 and by the University of Rochester. Additional support for participating scientists was provided by NSF and DOE (U.S.A.) by CAPES and CNPq (Brazil), by CoNaCyT (Mexico), by Proyecto Basal FB 0821, CONICYT PIA ACT1413, Fondecyt 3170845 and 11130133 (Chile), by PIIC (DGIP-UTFSM), by CONCYTEC, DGI-PUCP and IDI/IGI-UNI (Peru)Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concyte

Endoscopic Postoperative Recurrence in Crohn's Disease After Curative Ileocecal Resection with Early Prophylaxis by Anti-TNF, Vedolizumab or Ustekinumab: A Real-World Multicentre European Study.

BACKGROUND: Endoscopic-post-operative-recurrence [ePOR] in Crohn's disease [CD] after ileocecal resection [ICR] is a major concern. We aimed to evaluate the effectiveness of early prophylaxis with biologics and to compare anti-tumour necrosis factor [anti-TNF] therapy to vedolizumab [VDZ] and ustekinumab [UST] in a real-world setting. METHODS: A retrospective multicentre study of CD-adults after curative ICR on early prophylaxis was undertaken. ePOR was defined as a Rutgeerts score [RS] ≥ i2 or colonic-segmental-SES-CD ≥ 6. Multivariable logistic regression was used to evaluate risk factors, and inverse probability treatment weighting [IPTW] was applied to compare the effectiveness between agents. RESULTS: The study included 297 patients (53.9% males, age at diagnosis 24 years [19-32], age at ICR 34 years [26-43], 18.5% smokers, 27.6% biologic-naïve, 65.7% anti-TNF experienced, 28.6% two or more biologics and 17.2% previous surgery). Overall, 224, 39 and 34 patients received anti-TNF, VDZ or UST, respectively. Patients treated with VDZ and UST were more biologic experienced with higher rates of previous surgery. ePOR rates within 1 year were 41.8%. ePOR rates by treatment groups were: anti-TNF 40.2%, VDZ 33% and UST 61.8%. Risk factors for ePOR at 1 year were: past-infliximab (adjusted odds ratio [adj.OR] = 1.73 [95% confidence interval, CI: 1.01-2.97]), past-adalimumab [adj.OR = 2.32 [95% CI: 1.35-4.01] and surgical aspects. After IPTW, the risk of ePOR within 1 year of VDZ vs anti-TNF or UST vs anti-TNF was comparable (OR = 0.55 [95% CI: 0.25-1.19], OR = 1.86 [95% CI: 0.79-4.38]), respectively. CONCLUSION: Prevention of ePOR within 1 year after surgery was successful in ~60% of patients. Patients treated with VDZ or UST consisted of a more refractory group. After controlling for confounders, no differences in ePOR risk were seen between anti-TNF prophylaxis and other groups