The Natural Reproduction of the Cutthroat Trout, Salmo Clarki Richardson, in Strawberry Reservoir, Utah

Strawberry Reservoir, Utah is an 8.000 acre lake at the elevation of 7,550 feet; it has a maximum depth of 52 feet and an average depth of 18 feet. The supply of cutthroat trout eggs used to replenish and distribute this trout throughout the state are taken from two spawning traps located on reservoir tributaries. Because it has not proven economical to raise cutthroat to a larger size than newly hatched fry, it is at this site they are planted. Because of competition, predation, and lack of space the planting back of fry to maintain the fishery and spawning run presents a serious problem. Tributaries are at carrying capacity from being closed to fishing and the reservoir supports a tremendous population of trash fish; mainly the Utah chub, Gila atraria (Girard); yellow perch, Perca flavescens (Mitchell); redside shiner, Richardsonius balteatus (Cope); mountain sucker, Pantosteus delphinus Cope; and dace. Rhinichthys osculus Cope. The rainbow trout, Salmo gairdneri Richardson. and the cutthroat trout dominate the game fish population. It has been the policy of the Utah State Department of Fish and Game to trap and artificially spawn spawn every possible fish, leaving most of the spawning ground unused. This cost, plus the unknown advantage, if any, of artificial over natural reproduction led to the study of the possibilities of natural reproduction for replenishment of the cutthroat trout in the reservoir

Choices and Responsibilities: A Human Centric Approach to University-Industry Knowledge Transfer

The paper reflects on the growing complexities of management education in which business practitioners invite selected academic institutions to develop partnerships for resolving practical challenges and equipping those in the workplace to make more reflective and enlightened choices. European examples from Cambridge and Nottingham illustrate that successful industry and academic collaborations embody long-established themes of mentor-mentee, master-learner relationships. This human centric approach yields personal characteristics for reflective practitioners which enhance innovation, productivity and reputation building. The examples presented in this paper are then placed in a broader university – industry knowledge transfer context, using a so-called ‘bow tie’ model. The authors believe that by shifting the attention from processes to people, from productivity to individual and collective growth and maturity, and by starting to apply the best practices of our human heritage we can make a difference. It is the responsibility of all stakeholders of education to support and contribute to this shift

A “trigger”, a cause or obscured? How trauma and adversity are constructed in psychiatric stress-vulnerability accounts of “psychosis”

How do mental health professionals link adverse life experiences with the kinds of beliefs and experiences which attract a diagnosis of psychosis and what implications does this have for women with these diagnoses? Drawing on a broadly critical realist framework, we present data from two studies relevant to these questions. First, we analyse the discursive practices engaged in during a staff-only discussion of a female in-patient with a psychosis diagnosis who had been raped some years previously. Staff oriented to the irrationality and factuality of her ostensibly delusional statements about rape and pregnancy in the present and formulated adverse experience as a ‘stress factor’ triggering a manic episode, thereby precluding alternative contextualising interpretations. In a second, interview-based, study, psychiatrists drew on a range of discursive resources which differentiated psychosis from other forms of distress, constructed trauma as a stressor which could trigger psychosis because of a genetic predisposition, and constructed medication as the primary intervention whilst a focus on trauma was de-emphasised. We discuss the implications of these findings for the kinds of explanations and forms of help offered and suggest ways in which distress might be contextualised as well as possible future directions for feminist research and practic

Molecular dynamics simulation on the effect of transition metal binding to the N-terminal fragment of amyloid-β

We report molecular dynamics simulations of three possible adducts of Fe(II) to the N-terminal 1–16 fragments of the amyloid-β peptide, along with analogous simulations of Cu(II) and Zn(II) adducts. We find that multiple simulations from different starting points reach pseudo-equilibration within 100–300 ns, leading to over 900 ns of equilibrated trajectory data for each system. The specifics of the coordination modes for Fe(II) have only a weak effect on peptide secondary and tertiary structures, and we therefore compare one of these with analogous models of Cu(II) and Zn(II) complexes. All share broadly similar structural features, with mixture of coil, turn and bend in the N-terminal region and helical structure for residues 11–16. Within this overall pattern, subtle effects due to changes in metal are evident: Fe(II) complexes are more compact and are more likely to occupy bridge and ribbon regions of Ramachandran maps, while Cu(II) coordination leads to greater occupancy of the poly-proline region. Analysis of representative clusters in terms of molecular mechanics energy and atoms-in-molecules properties indicates similarity of four-coordinate Cu and Zn complexes, compared to five-coordinate Fe complex that exhibits lower stability and weaker metal–ligand bonding

Ligand field molecular dynamics simulation of Pt(II)-phenanthroline binding to N-terminal fragment of amyloid-β peptide

We report microsecond timescale molecular dynamics simulation of the complex formed between Pt(II)-phenanthroline and the 16 N-terminal residues of the Aβ peptide that is implicated in the onset of Alzheimer’s disease, along with equivalent simulations of the metal-free peptide. Simulations from a variety of starting points reach equilibrium within 100 ns, as judged by root mean square deviation and radius of gyration. Platinum-bound peptides deviate rather more from starting points, and adopt structures with larger radius of gyration, than their metal-free counterparts. Residues bound directly to Pt show smaller fluctuation, but others actually move more in the Pt-bound peptide. Hydrogen bonding within the peptide is disrupted by binding of Pt, whereas the presence of salt-bridges are enhanced

Metal binding to amyloid-β1–42: a ligand field molecular dynamics study

Ligand field molecular mechanics simulation has been used to model the interactions of copper(II) and platinum(II) with the amyloid-β1–42 peptide monomer. Molecular dynamics over several microseconds for both metalated systems are compared to analogous results for the free peptide. Significant differences in structural parameters are observed, both between Cu and Pt bound systems as well as between free and metal-bound peptide. Both metals stabilize the formation of helices in the peptide as well as reducing the content of β secondary structural elements compared to the unbound monomer. This is in agreement with experimental reports of metals reducing β-sheet structures, leading to formation of amorphous aggregates over amyloid fibrils. The shape and size of the peptide structures also undergo noteworthy change, with the free peptide exhibiting globular-like structure, platinum(II) system adopting extended structures, and copper(II) system resulting in a mixture of conformations similar to both of these. Salt bridge networks exhibit major differences: the Asp23-Lys28 salt bridge, known to be important in fibril formation, has a differing distance profile within all three systems studied. Salt bridges in the metal binding region of the peptide are strongly altered; in particular, the Arg5-Asp7 salt bridge, which has an occurrence of 71% in the free peptide, is reduced to zero in the presence of both metals

Benchmarking of copper(II) LFMM parameters for studying amyloid-β peptides

Ligand field molecular mechanics (LFMM) parameters have been benchmarked for copper (II) bound to the amyloid-β1–16 peptide fragment. Several density functional theory (DFT) optimised small test models, representative of different possible copper coordination modes, have been used to test the accuracy of the LFMM copper bond lengths and angles, resulting in errors typically less than 0.1 Å and 5°. Ligand field molecular dynamics (LFMD) simulations have been carried out on the copper bound amyloid-β1–16 peptide and snapshots extracted from the subsequent trajectory. Snapshots have been optimised using DFT and the semi-empirical PM7 method resulting in good agreement against the LFMM calculated geometry. Analysis of substructures within snapshots shows that the larger contribution of geometrical difference, as measured by RMSD, lies within the peptide backbone, arising from differences in DFT and AMBER, and the copper coordination sphere is reproduced well by LFMM. PM7 performs excellently against LFMM with an average RMSD of 0.2 Å over 21 tested snapshots. Further analysis of the LFMD trajectory shows that copper bond lengths and angles have only small deviations from average values, with the exception of a carbonyl moiety from the N-terminus, which can act as a weakly bound fifth ligand

The use of machine learning to understand the relationship between IgE to specific allergens and asthma

In a study recently published in PLOS Medicine, Custovic and colleagues report an elegant analysis of the results of specific Immunoglobulin E (IgE) assays on sera from a birth cohort that they have studied in detail over the last 15 years. The IgE assays used a microchip with >100 purified allergens, and their machine learning approach used a hypothesis-free statistical approach to group children with similar allergen sensitization profiles. The primary objective was to evaluate the relationship of the results to the diagnosis of current asthma. The goal of employing a “machine learning” approach is both as a form of data reduction to avoid spuriously identified associations resulting from multiple comparisons and to identify biologically relevant groupings that may not be recognized with conventional approaches. The use of “hypothesis free” clustering methods with individual allergen components in itself is not novel. However, the authors have employed a new network analysis method, which is improved over previous methods because it can capture nonlinear relationships and does not rely on assumption of a parametric probability. As the authors show, this approach can lead to improved sensitivity and specificity. With this model, they were able to show that pairings of allergens (e.g., the cat allergen Fel d 1 and the peanut allergen Ara h 1) were more predictive of the child having asthma than individual components, although we must be cautious in the generalizability of the specific patterns identified, in this single cohort in the United Kingdom, because the pairing may represent host susceptibility to making an IgE response or common co-exposures, which could be allergens or adjuvants. Still, the concept of complicated connections between individual allergenic proteins and allergic disease are likely to apply more broadly and are important to consider in future studies