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The use of deep eutectic solvent (DES)/water mixtures were explored for the selective enzymatic synthesis of α‐monobenzoate glycerol (α‐MBG) from glycerol and benzoic acid as substrates. Experiments were performed with four DES, three of them containing choline chloride (ChCl), combined with urea (URA), glycerol (GLY), and ethylene glycol (ETA) (in all cases ChCl/HBD 1:2 mol ratio), and another one formed with methylammonium chloride and glycerol (MA/GLY 1:3 mol ratio). The best conversions (99 %) were achieved with immobilized lipase B from Candida antarctica (CAL‐B) when ChCl/GLY was used as the solvent and the substrate at the same time. The use of water as a cosolvent (8 % v/v) led to a significant decrease in the viscosity of the DES, and full conversions were then reached. Reusability studies of the biocatalyst revealed a 37 % decrease in activity after the first batch, but the activity remained mostly constant for the rest of the cycles.

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Despite their importance for stakeholders in the criminal justice system, few methods have been developed for determining which criminal behavior variables will produce accurate sentence predictions. Some approaches found in the literature resort to techniques based on indirect variables, but not on the social network behavior with exception of the work of Baker and Faulkner [ASR 58: 837–860, 1993]. Using information on the Caviar Network narcotics trafficking group as a real-world case, we attempt to explain sentencing outcomes employing the social network indicators. Specifically, we report the ability of centrality measures to predict a) the verdict (innocent or guilty) and b) the sentence length in years. We show that while the set of indicators described by Baker and Faulkner yields good predictions, introduction of the additional centrality measures generates better predictions. Some ideas for orienting future research on further improvements to sentencing outcome prediction are discussed.A pesar de la importancia para diferentes actores involucrados en el sistema judicial, se han desarrollados pocos métodos para determinar las variables del comportamiento organizado que permiten predecir las sentencias judiciales de redes criminales. Algunas aproximaciones encontradas en la literatura especializada usa variables indirectas al comportamiento organizado y no en el comportamiento en red de estas organizaciones. Nosotros usamos información real sobre un caso de red criminal real que operó en Montreal (Canadá) y analizamos la comunicación entre los miembros de la red para determinar si su comportamiento comunicacional permite predecir el veredicto así como los años de sentencia. Encontramos que los modelos de regresión obtenidos y las variables de centralidad nodal utilizadas por nosotros logra un mejor capacidad predictiva. Finalmente, se discuten algunas ideas dirigidas a mejorar la predicción de sentencias judiciales desde las medidas de redes sociales

Daily glucose variability is higher in diabetic mellitus (DM) patients which has been related to the severity of the disease. However, it is unclear whether glycemic variability displays a specific pattern oscillation or if it is completely random. Thus, to determine glycemic variability pattern, we measured and analyzed continuous glucose monitoring (CGM) data, in control subjects and patients with DM type-1 (T1D). CGM data was assessed for 6 days (day: 08:00–20:00-h; and night: 20:00–08:00-h). Participants (n = 172; age = 18–80 years) were assigned to T1D (n = 144, females = 65) and Control (i.e., healthy; n = 28, females = 22) groups. Anthropometry, pharmacologic treatments, glycosylated hemoglobin (HbA1c) and years of evolution were determined. T1D females displayed a higher glycemia at 10:00–14:00-h vs. T1D males and Control females. DM patients displays mainly stationary oscillations (deterministic), with circadian rhythm characteristics. The glycemia oscillated between 2 and 6 days. The predictive model of glycemia showed that it is possible to predict hyper and hypoglycemia (R2 = 0.94 and 0.98, respectively) in DM patients independent of their etiology. Our data showed that glycemic variability had a specific oscillation pattern with circadian characteristics, with episodes of hypoglycemia and hyperglycemia at day phases, which could help therapeutic action for this population.

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The aim of this paper was to create a decision tree (DT) to identify personality profiles of offenders against public safety. A technique meeting this requirement was proposed that uses the C4.5 algorithm to derive decision rules for personality profiling of public safety offenders. The Mini-Mult test was used to measure the personality profiles of 238 individuals. With the test results as our database, a C4.5 DT was applied to construct rules that classify each profile into one of two groups, those without and those with records of offences against public safety. The model correctly classified 80% of the personality profiles and delivered a set of decision rules for distinguishing the profiles by group, and the principal personality profiles were interpreted. We conclude that DTs are a promising technique for analysing personality profiles by their offender or non-offender status. Finally, we believe that the development of a classifying model using DT may have practical applications in the Colombian prison syste