Agrobacterial rol genes modify thermodynamic and structural properties of starch in microtubers of transgenic potato

Wild-type (WT) plants of potato (Solanum tuberosum L.) and their transgenic forms carrying agrobacterial genes rolB or rolC under the control of B33 class I patatin promoter were cultured in vitro on MS medium with 2% sucrose in a controlled-climate chamber at 16-h illumination and 22A degrees C. These plants were used as a source of single-node stem cuttings, which were cultured in darkness on the same medium supplemented with 8% sucrose. The tubers formed on them were used for determination of the structure of native starch using the methods of differential scanning microcalorimetry (DSC), X-ray scattering, and scanning electron microscopy. It was found that, in starch from the tubers of rolB-plants, the temperature of crystalline lamella melting was lower and their thickness was less than in WT potato. In tubers of rolC plants, starch differed from starch in WT plants by a higher melting temperature, considerably reduced melting enthalpy, and a greater thickness of crystalline lamellae. Deconvolution of DSC thermogram makes it possible to interpret the melting of starch from the tubers of rolC plants as the melting of two independent crystalline structures with melting temperatures of 65.0 and 69.8A degrees C. Electron microscopic examination confirmed the earlier obtained data indicating that, in the tubers of rolC plants, starch granules are smaller and in the tubers of rolB plants larger than in WT plants. Possible ways of influence of rol transgenes on structural properties of starch in amyloplasts of potato tubers are discusse

Effects of Agrobacterial rol-Genes on the Thermodynamic and Structural Features of Starches Extracted from Potato Microtubers

Wild-type potato (Solanum tuberosum L.) plants and their transformants harboring agrobacterial rolB or rolC genes under control of the patatin class I promoter were cultured in vitro. These plants were used as a source of single-node stem cuttings. The structure of native starch in tubers formed on cuttings was determined using methods of X-ray scattering and differential scanning microcalorimetry (DSC). It was found that in starch from tubers of rolB plants the melting temperature of crystalline lamella was lower and their thickness was less than that in wild-type potato. In tubers of rolC plants starch differed from starch in wild-type plants by a higher melting temperature, reduced melting enthalpy, and a greater thickness of crystalline lamellae. The melting of starch from tubers of rolC plants proceeded as the melting of two independent crystalline structures with melting temperatures of 338.0°K and 342.8°K. Overall data show that starches of different structure can be obtained by using transgenic approac

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Scalability considerations for multivariate graph visualization

Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Comorbid Internalizing and Disruptive Behavior Disorder in Adolescents: Offending, Trauma, and Clinical Characteristics

This study examined differences between comorbid internalizing and disruptive behavior disorder (DBD), and those with either internalizing disorder or DBD. We focused on differences with regard to trauma exposure and offending characteristics in 8,431 juvenile justice youths. Self-reported, structured interview and official record data were used. Multinomial logistic regression analysis predicted disorder profile from traumatic exposure, suicide attempt, and offending characteristics, adjusting for background variables. Victimization by non-sexual violence was significantly higher in comorbid than in internalizing youth. Also, the number of DBDs, as well as rates of victimization via sexual and non-sexual assault, was significantly higher in the comorbid than in the DBD group. We conclude that a history of victimization, but not an early onset of criminal behavior, was associated with comorbid internalizing disorder and DBD. Findings emphasize the need for improving identification of this comorbid condition and referral for effective treatment

Service referral for juvenile justice youths: associations with psychiatric disorder and recidivism

Secondary multiple regression analyses related disorder profile, probation officers' mental health/substance use service referrals, and recidivism in 361 juvenile justice youths. Those with externalizing (disruptive behavior or substance use) disorder or substance offenses were most likely to receive service referrals. Substance disordered youths with service referrals had lower recidivism risk compared to counterparts without referrals; referral lowered the recidivism odds to approximately that for youths without a substance use disorder. Providing juvenile justice youths with systematic mental health assessment and linking those with substance use disorder to mental health and substance use services likely reduces recidivism risk