Quantum Sensing in a Physiological‐Like Cell Niche Using Fluorescent Nanodiamonds Embedded in Electrospun Polymer Nanofibers

Fluorescent nanodiamonds (fNDs) containing nitrogen vacancy (NV) centers are promising candidates for quantum sensing in biological environments. This work describes the fabrication and implementation of electrospun poly lactic‐co‐glycolic acid (PLGA) nanofibers embedded with fNDs for optical quantum sensing in an environment, which recapitulates the nanoscale architecture and topography of the cell niche. A protocol that produces uniformly dispersed fNDs within electrospun nanofibers is demonstrated and the resulting fibers are characterized using fluorescent microscopy and scanning electron microscopy (SEM). Optically detected magnetic resonance (ODMR) and longitudinal spin relaxometry results for fNDs and embedded fNDs are compared. A new approach for fast detection of time varying magnetic fields external to the fND embedded nanofibers is demonstrated. ODMR spectra are successfully acquired from a culture of live differentiated neural stem cells functioning as a connected neural network grown on fND embedded nanofibers. This work advances the current state of the art in quantum sensing by providing a versatile sensing platform that can be tailored to produce physiological‐like cell niches to replicate biologically relevant growth environments and fast measurement protocols for the detection of co‐ordinated endogenous signals from clinically relevant populations of electrically active neuronal circuits.     

Control of reactive distillation production of high-purity isopropanol

The process characteristics and control strategy of a high-purity IPA reactive distillation column were investigated. A robust nominal operation was found by maintaining an excess of propylene feed to the column and recycling the unreacted propylene to the feed instead of the top stage. Stage temperature and propylene composition with one-to-one relationship with reboiler duty and propylene feed are selected as controlled variables for maintaining bottom purity and feed ratio in the presence of possible measurement bias respectively. High nonlinearity between selected input–output pair was reduced by using variable transformation. Dynamic simulations demonstrated that such a control scheme with nonlinear transformed variable was capable of providing much superior control performance than the one using natural variable.     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

Parameter Uncertainty and Policy Intensity: Some Extensions and Suggestions for Further Work

The effects of parameter uncertainty on optimal policy have been a matter of interest for academics, and even for some policymakers, for a long time. Two lines of literature have developed analytical results on this matter. The first line uses static models and the second dynamic models. In this dynamic line most of the results are confined to models with a single state and a single control variable. In this paper we want to encourage the analysis of more general dynamic cases. To do so, the results in the dynamic line are extended from one-state and one-control finite horizon models to models with a pair of control variables. We then discuss some of the hurdles which must be surmounted for the results to be made more general and suggests some lines for further research. JEL classification: C61; E61     

The effects of nonlocal impact ionization on the speed of avalanche photodiodes

The nonlocal enhancement in the velocities of charge carriers to ionization is shown to outweigh the opposing effects of dead space, increasing the avalanche speed of short avalanche photodiodes (APDs) over the predictions of a conventional local model which ignores both of these effects. The trends in the measured gain-bandwidth product of two short InAlAs APDs reported in the literature support this result. Relatively large speed benefits are predicted to result from further small reductions in the lengths of short multiplication regions.     

Effect of pulse time gate on CsI(T1) light output

The mass and charge identification of secondary particles with Z < 4 by a large CsI(T1) scintillation detector is performed using pulse shape analysis and time-of-flight methods. The dependence of the light output on E, A and Z is studied in the energy range of 1–20 MeV/A and special attention is paid to the integration time of the photomultiplier anode signal. It is found that the behaviour of the calibration curves strongly depends on the choice of the integration time interval.     

Blood pressure estimation and beliefs among normotensives and hypertensives.

14 medicated hypertensive, 15 nonmedicated mild hypertensive, 39 normotensive, and 13 hypotensive adults participated in a 1–2 hr laboratory experiment that assessed each S's symptoms, moods, and estimates of systolic blood pressure (SBP) relative to actual SBP levels. Several self-reports and autonomic measures were collected during and after each of 22 tasks. Within-S correlations indicated that all Ss could estimate SBP at levels greater than chance. Further, 68% of the Ss evidenced at least 1 significant symptom–SBP correlation. Although medicated hypertensives believed they could estimate their BP more accurately than other groups, they were actually no more accurate than the other Ss. They also evidenced far fewer empirically derived symptom–SBP and emotion–SBP correlations than any other group. Overall, blood pressure (BP) beliefs were largely inaccurate. If these erroneous beliefs can be eliminated, Ss may be able to estimate BP fluctuations more accurately. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Peer consultation groups for psychologists in private practice: A national survey.

We report the results of the first national survey of psychologists in private practice regarding their participation in peer consultation groups. The sample (71% return) was drawn from 800 randomly selected psychologists listed in the National Register of Health Service Providers in Psychology. We found that 23% of the sample currently belonged to peer consultation groups, and 24% had belonged in the past. Of those not currently in groups, 61% expressed the desire to belong if one were available. There were virtually no significant demographic differences between current members and nonmembers. We examined the following group characteristics: formation, length of existence, size, membership, leadership, theoretical orientation, range of experience, time and place, content, and group process. Groups tended to be small, informal, and leaderless; however, we found great variation among groups on all dimensions. Findings also showed a high degree of satisfaction with membership. (PsycINFO Database Record (c) 2010 APA, all rights reserved)