As the number falls,alternatives to the Hagberg–Perten falling number method: A review

This review examines the application, limitations, and potential alternatives to the Hagberg–Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.     

Characterization and Reconstitution of Human Lipoyl Synthase (LIAS) Supports ISCA2 and ISCU as Primary Cluster Donors and an Ordered Mechanism of Cluster Assembly

Lipoyl synthase (LIAS) is an iron–sulfur cluster protein and a member of the radical S-adenosylmethionine (SAM) superfamily that catalyzes the final step of lipoic acid biosynthesis. The enzyme contains two [4Fe–4S] centers (reducing and auxiliary clusters) that promote radical formation and sulfur transfer, respectively. Most information concerning LIAS and its mechanism has been determined from prokaryotic enzymes. Herein, we detail the expression, isolation, and characterization of human LIAS, its reactivity, and evaluation of natural iron–sulfur (Fe–S) cluster reconstitution mechanisms. Cluster donation by a number of possible cluster donor proteins and heterodimeric complexes has been evaluated. [2Fe–2S]-cluster-bound forms of human ISCU and ISCA2 were found capable of reconstituting human LIAS, such that complete product turnover was enabled for LIAS, as monitored via a liquid chromatography–mass spectrometry (LC–MS) assay. Electron paramagnetic resonance (EPR) studies of native LIAS and substituted derivatives that lacked the ability to bind one or the other of LIAS’s two [4Fe–4S] clusters revealed a likely order of cluster addition, with the auxiliary cluster preceding the reducing [4Fe–4S] center. These results detail the trafficking of Fe–S clusters in human cells and highlight differences with respect to bacterial LIAS analogs. Likely in vivo Fe–S cluster donors to LIAS are identified, with possible connections to human disease states, and a mechanistic ordering of [4Fe–4S] cluster reconstitution is evident.     

Free-living inferential modeling of blood glucose level using only noninvasive inputs

The goal of this work is to present a causation modeling methodology with the ability to accurately infer blood glucose levels using a large set of highly correlated noninvasive input variables over an extended period of time. These models can provide insight to improve glucose monitoring, and glucose regulation through advanced model-based control technologies. The efficacy of this approach is demonstrated using real data from a type 2 diabetic (T2D) subject collected under free-living conditions over a period of 25 consecutive days. The model was identified and tested using eleven variables that included three food variables as well as several activity and stress variables. The model was trained using 20 days of data and validated using 5 days of data. This gave a fitted correlation coefficient of 0.70 and an average absolute error (AAE) (i.e., the average of the absolute values for the measured glucose concentration minus modeled glucose concentration) of 13.3 mg/dL for the validation data. This AAE result was significantly better than the subject’s personal glucose meter AAE of 15.3 mg/dL for replicated measurements.     

Eyewitness Identification Accuracy and Response Latency: The Unruly 10-12-Second Rule.

Data are reported from 3,213 research eyewitnesses confirming that accurate eyewitness identifications from lineups are made faster than are inaccurate identifications. However, consistent with predictions from the recognition and search literatures, the authors did not find support for the "10-12-s rule" in which lineup identifications faster than 10-12 s maximally discriminate between accurate and inaccurate identifications (D. Dunning & S. Perretta, 2002). Instead, the time frame that proved most discriminating was highly variable across experiments, ranging from 5 s to 29 s, and the maximally discriminating time was often unimpressive in its ability to sort accurate from inaccurate identifications. The authors suggest several factors that are likely to moderate the 10-12-s rule. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Strain rate- and temperature-dependent tensile properties of an epoxy-based, thermosetting, shape memory polymer (Veriflex-E)

In this study, the inelastic deformation behavior of an epoxy-based, thermally triggered shape memory polymer resin, known as Veriflex-E, was investigated. The experimental program was designed to explore the influence of strain rate on monotonic loading at various temperatures which is needed to establish the design space of SMPs in load bearing applications. Thermally actuated shape memory polymers can be thought of as having two phases separated by the glass transition temperature (T _g ). At temperatures below the T _g , Veriflex-E exhibits a high elastic modulus and positive, non-linear strain rate sensitivity in monotonic loading. The Poisson’s ratio at room temperature is independent of the strain rate, but dependent upon the strain magnitude. As the temperature is increased, the strain rate sensitivity in monotonic loading decreases. Well above the T _g , the elastic modulus drops by several orders of magnitude. In this high temperature region, the material achieves strain levels well above 100% and Poisson’s ratio is constant at 0.5 regardless of strain rate or strain magnitude.     

A thermomechanical constitutive model for an epoxy based shape memory polymer and its parameter identifications

This paper presents a three-dimensional (3D) finite deformation thermomechanical model to study the glass transition and shape memory behaviors of an epoxy based shape memory polymer (SMP) (Veriflex E) and a systematic material parameter identification scheme from a set of experiments. The model was described by viscoelastic elements placed in parallel to represent different active relaxation mechanisms around glass transition temperature in the polymer. A set of standard material tests was proposed and conducted to identify the model parameter values, which consequently enable the model to reproduce the experimentally observed shape memory (SM) behaviors. The parameter identification procedure proposed in this paper can be used as an effective tool to assist the construction and application of such 3D multi-branch model for general SMP materials.     

Oxidation Behavior of Aerospace Materials in High Enthalpy Flows Using an Oxyacetylene Torch Facility

The oxyacetylene torch facility is used to measure the ablation rates of graphite and the surface temperatures of different aerospace materials. The free‐stream flame environment is characterized as a function of flame chemistry for heat flux, pO₂, and flow velocity. Measured ablation rates for graphite increase as a function of increasing heat flux and pO₂, which are validated by applying an oxygen diffusion based model. The model uses experimentally measured values for temperature, pO₂, and gas velocity in order to confirm torch testing results are reliable and reproducible. Surface temperatures of ultra‐high temperature ceramic composites are measured as a function of increasing heat flux and show an enthalpic cooling effect on the flame during oxidation testing.     

Altruism on American Television: Examining the Amount of, and Context Surrounding, Acts of Helping and Sharing

Using a representative sample of television content featuring 2,227 programs across different genres and 18 different channels, the frequency and context of altruistic actions were content analyzed. A social cognitive theory approach was taken to guide the selection of contextual variables. The results showed that 73% of the programs in the sample featured instances of helping/sharing at a rate of 2.92 incidents per hour. Further, the actions were most likely to be initiated by adult White males and many acts were depicted in a humorous, realistic, and rewarding context. In terms of channel differences, shows on children's basic cable featured not only the highest proportion of programs with one or more instances of altruism but also the highest rate per hour (4.02 acts). Differences in contextual features and channel type are discussed in terms of social cognitive theory below.