Water uptake and transpiration were studied through the surface of intact sweet cherry (Prunus avium L.) fruit, exocarp segments (ES) and cuticular membranes (CM) excised from the cheek of sweet cherry fruit and astomatous CM isolated from Schefflera arboricola (Hayata) Hayata, Citrus aurantium L., and Stephanotis floribunda Brongn. leaves or from Lycopersicon esculentum Mill. and Capsicum annuum L. var. annuum Fasciculatum Group fruit. ES and CM were mounted in diffusion cells. Water (deionized) uptake into intact sweet cherry fruit, through ES or CM interfacing water as a donor and a polyethyleneglycol (PEG 6000, osmotic pressure 2.83 MPa)-containing receiver was determined gravimetrically. Transpiration was quantified by monitoring weight loss of a PEG 6000-containing donor (2.83 MPa) against dry silica as a receiver. The permeability coefficients for osmotic water uptake and transpiration were calculated from the amount of water taken up or transpired per unit surface area and time, and the driving force for transport. Permeability during osmotic water uptake was markedly higher than during transpiration in intact sweet cherry fruit (40.2-fold), excised ES of sweet cherry fruit (12.5- to 53.7-fold) and isolated astomatous fruit and leaf CM of a range of species (on average 23.0-fold). Partitioning water transport into stomatal and cuticular components revealed that permeability of the sweet cherry fruit cuticle for water uptake was 11.9-fold higher and that of stomata 56.8-fold higher than the respective permeability during transpiration. Increasing water vapor activity in the receiver from 0 to 1 increased permeability during transpiration across isolated sweet cherry fruit CM about 2.1-fold. Permeability for vapor uptake from saturated water vapor into a PEG 6000 receiver solution was markedly lower than from liquid water, but of similar magnitude to the permeability during self-diffusion of (3)H(2)O in the absence of osmotica. The energy of activation for self-diffusion of water across ES or CM was higher than for osmotic water uptake and decreased with increasing stomatal density. The data indicate that viscous flow along an aqueous continuum across the sweet cherry fruit exocarp and across the astomatous CM of selected species accounted for the higher permeability during water uptake as compared to self-diffusion or transpiration.

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This study employs machine learning algorithms to examine the causes for engaging in violent offending in individuals with schizophrenia spectrum disorders. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy, Zurich University Hospital of Psychiatry, Switzerland. Based on findings of the general strain theory and using logistic regression and machine learning algorithms, it was analyzed whether accumulation and type of stressors in the inpatients’ history influenced the severity of an offense. A higher number of stressors led to more violent offenses, and five types of stressors were identified as being highly influential regarding violent offenses. Our findings suggest that an accumulation of stressful experiences in the course of life and certain types of stressors might be particularly important in the development of violent offending in individuals suffering from schizophrenia spectrum disorders. A better understanding of risk factors that lead to violent offenses should be helpful for the development of preventive and therapeutic strategies for patients at risk and could thus potentially reduce the prevalence of violent offenses.

Linear statistical methods may not be suited to the understanding of psychiatric phenomena such as aggression due to their complexity and multifactorial origins. Here, the application of machine learning (ML) algorithms offers the possibility of analyzing a large number of influencing factors and their interactions. This study aimed to explore inpatient aggression in offender patients with schizophrenia spectrum disorders (SSDs) using a suitable ML model on a dataset of 370 patients. With a balanced accuracy of 77.6% and an AUC of 0.87, support vector machines (SVM) outperformed all the other ML algorithms. Negative behavior toward other patients, the breaking of ward rules, the PANSS score at admission as well as poor impulse control and impulsivity emerged as the most predictive variables in distinguishing aggressive from non-aggressive patients. The present study serves as an example of the practical use of ML in forensic psychiatric research regarding the complex interplay between the factors contributing to aggressive behavior in SSD. Through its application, it could be shown that mental illness and the antisocial behavior associated with it outweighed other predictors. The fact that SSD is also highly associated with antisocial behavior emphasizes the importance of early detection and sufficient treatment.