Combination of automated solid-phase and enzymatic oligosaccharide synthesis provides access to α(2,3)-sialylated glycans

A synthetic strategy combining automated solid-phase chemical synthesis and enzymatic sialylation was developed to access α(2,3)-sialylated glycans.</p

Systematic Hydrogen‐Bond Manipulations To Establish Polysaccharide Structure–Property Correlations

A dense hydrogen‐bond network is responsible for the mechanical and structural properties of polysaccharides. Random derivatization alters the properties of the bulk material by disrupting the hydrogen bonds, but obstructs detailed structure–function correlations. We have prepared well‐defined unnatural oligosaccharides including methylated, deoxygenated, deoxyfluorinated, as well as carboxymethylated cellulose and chitin analogues with full control over the degree and pattern of substitution. Molecular dynamics simulations and crystallographic analysis show how distinct hydrogen‐bond modifications drastically affect the solubility, aggregation behavior, and crystallinity of carbohydrate materials. This systematic approach to establishing detailed structure–property correlations will guide the synthesis of novel, tailor‐made carbohydrate materials

Systematic Hydrogen‐Bond Manipulations To Establish Polysaccharide Structure–Property Correlations

A dense hydrogen‐bond network is responsible for the mechanical and structural properties of polysaccharides. Random derivatization alters the properties of the bulk material by disrupting the hydrogen bonds, but obstructs detailed structure–function correlations. We have prepared well‐defined unnatural oligosaccharides including methylated, deoxygenated, deoxyfluorinated, as well as carboxymethylated cellulose and chitin analogues with full control over the degree and pattern of substitution. Molecular dynamics simulations and crystallographic analysis show how distinct hydrogen‐bond modifications drastically affect the solubility, aggregation behavior, and crystallinity of carbohydrate materials. This systematic approach to establishing detailed structure–property correlations will guide the synthesis of novel, tailor‐made carbohydrate materials

Architecture for Control of the K9 Rover

Software featuring a multilevel architecture is used to control the hardware on the K9 Rover, which is a mobile robot used in research on robots for scientific exploration and autonomous operation in general. The software consists of five types of modules: Device Drivers - These modules, at the lowest level of the architecture, directly control motors, cameras, data buses, and other hardware devices. Resource Managers - Each of these modules controls several device drivers. Resource managers can be commanded by either a remote operator or the pilot or conditional-executive modules described below. Behaviors and Data Processors - These modules perform computations for such functions as planning paths, avoiding obstacles, visual tracking, and stereoscopy. These modules can be commanded only by the pilot. Pilot - The pilot receives a possibly complex command from the remote operator or the conditional executive, then decomposes the command into (1) more-specific commands to the resource managers and (2) requests for information from the behaviors and data processors. Conditional Executive - This highest-level module interprets a command plan sent by the remote operator, determines whether resources required for execution of the plan are available, monitors execution, and, if necessary, selects an alternate branch of the plan

At risk of being risky: The relationship between "brain age" under emotional states and risk preference.

Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N=212; 10-25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the "brain age" of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that "brain age" across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts) relates to a group mean difference in risk perception - a pattern exemplified greatest in young-adults (ages 18-21). The results are suggestive of a specified functional brain phenotype that relates to being at "risk to be risky.