An exploratory study on the contribution of graduate entry students personality to the diversity of medical student populations

Studies conducted in medical education show that personality influences undergraduate medical students academic and clinical performances and also their career interests. Our aims with this exploratory study were: to assess the contribution of graduate entry students to the diversity of personality in medical student populations; to assess whether eventual differences may be explained by programme structure or student age and sex. We performed a cross-sectional study underpinned by the five-factor model of personality, with students attending three medical schools in Portugal. The five personality dimensions were assessed with the Portuguese version of the NEO-Five Factor Inventory. MANOVA and MANCOVA analyses were performed to clarify the contributions of school, programme structure, age and sex. Student personality dimensions were significantly different between the three medical schools [F (10,1026)  = 3.159, p < .001, [Formula: see text] = 0.03, π = 0.987]. However, taking sex and age into account the differences became non-significant. There were institutional differences in personality dimensions. However, those were primarily accounted for by sex and age effects and not by the medical school attended. Diversifying age and sex of the admitted students will diversify the personality of the medical student population

Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration

Reconstruction and measurement of (100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics

Cryptosporidium Priming Is More Effective than Vaccine for Protection against Cryptosporidiosis in a Murine Protein Malnutrition Model

Cryptosporidium is a major cause of severe diarrhea, especially in malnourished children. Using a murine model of C. parvum oocyst challenge that recapitulates clinical features of severe cryptosporidiosis during malnutrition, we interrogated the effect of protein malnutrition (PM) on primary and secondary responses to C. parvum challenge, and tested the differential ability of mucosal priming strategies to overcome the PM-induced susceptibility. We determined that while PM fundamentally alters systemic and mucosal primary immune responses to Cryptosporidium, priming with C. parvum (106 oocysts) provides robust protective immunity against re-challenge despite ongoing PM. C. parvum priming restores mucosal Th1-type effectors (CD3+CD8+CD103+ T-cells) and cytokines (IFNγ, and IL12p40) that otherwise decrease with ongoing PM. Vaccination strategies with Cryptosporidium antigens expressed in the S. Typhi vector 908htr, however, do not enhance Th1-type responses to C. parvum challenge during PM, even though vaccination strongly boosts immunity in challenged fully nourished hosts. Remote non-specific exposures to the attenuated S. Typhi vector alone or the TLR9 agonist CpG ODN-1668 can partially attenuate C. parvum severity during PM, but neither as effectively as viable C. parvum priming. We conclude that although PM interferes with basal and vaccine-boosted immune responses to C. parvum, sustained reductions in disease severity are possible through mucosal activators of host defenses, and specifically C. parvum priming can elicit impressively robust Th1-type protective immunity despite ongoing protein malnutrition. These findings add insight into potential correlates of Cryptosporidium immunity and future vaccine strategies in malnourished children

Improved Weighted Random Forest for Classification Problems

Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models. The key to make well-performing ensemble model is in the diversity of the base models. Of the most common solutions for introducing diversity into the decision trees are bagging and random forest. Bagging enhances the diversity by sampling with replacement and generating many training data sets, while random forest adds selecting a random number of features as well. This has made the random forest a winning candidate for many machine learning applications. However, assuming equal weights for all base decision trees does not seem reasonable as the randomization of sampling and input feature selection may lead to different levels of decision-making abilities across base decision trees. Therefore, we propose several algorithms that intend to modify the weighting strategy of regular random forest and consequently make better predictions. The designed weighting frameworks include optimal weighted random forest based on ac-curacy, optimal weighted random forest based on the area under the curve (AUC), performance-based weighted random forest, and several stacking-based weighted random forest models. The numerical results show that the proposed models are able to introduce significant improvements compared to regular random forest

Clinical factors associated with a conservative gait pattern in older male veterans with diabetes

<p>Abstract</p> <p>Background</p> <p>Patients with diabetes and peripheral neuropathy are at higher risk for falls. People with diabetes sometimes adopt a more conservative gait pattern with decreased walking speed, widened base, and increased double support time. The purpose of this study was to use a multivariate approach to describe this conservative gait pattern.</p> <p>Methods</p> <p>Male veterans (mean age = 67 years; SD = 9.8; range 37–86) with diabetes (n = 152) participated in this study from July 2000 to May 2001 at the Veterans Affairs Medical Center, White River Junction, VT. Various demographic, clinical, static mobility, and plantar pressure measures were collected. Conservative gait pattern was defined by visual gait analysis as failure to demonstrate a heel-to-toe gait during the propulsive phase of gait.</p> <p>Results</p> <p>Patients with the conservative gait pattern had lower walking speed and decreased stride length compared to normal gait. (0.68 m/s v. 0.91 m/s, <it>p </it>< 0.001; 1.04 m v. 1.24 m, <it>p </it>< 0.001) Age, monofilament insensitivity, and Romberg's sign were significantly higher; and ankle dorsiflexion was significantly lower in the conservative gait pattern group. In the multivariate analysis, walking speed, age, ankle dorsiflexion, and callus were retained in the final model describing 36% of the variance. With the inclusion of ankle dorsiflexion in the model, monofilament insensitivity was no longer an independent predictor.</p> <p>Conclusion</p> <p>Our multivariate investigation of conservative gait in diabetes patients suggests that walking speed, advanced age, limited ankle dorsiflexion, and callus describe this condition more so than clinical measures of neuropathy.</p

Does familial risk for alcohol use disorder predict alcohol hangover?

Positive family history of alcohol use disorder (FHP), a variable associated with propensity for alcohol use disorder (AUD), has been linked with elevated hangover frequency and severity, after controlling for alcohol use. This implies that hangover experiences may be related to AUD. However, inadequate control of alcohol consumption levels, low alcohol dose and testing for hangover during the intoxication phase detract from these findings. Here, we present further data pertinent to understanding the relationship between family history and alcohol hangover. Study 1 compared past year hangover frequency in a survey of 24 FHP and 118 family history negative (FHN) individuals. Study 2 applied a quasi-experimental naturalistic approach assessing concurrent hangover severity in 17 FHP and 32 FHN individuals the morning after drinking alcohol. Both studies applied statistical control for alcohol consumption levels. In Study 1, both FHP status and estimated blood alcohol concentration on the heaviest drinking evening of the past month predicted the frequency of hangover symptoms experienced over the previous 12 months. In Study 2, estimated blood alcohol concentration the previous evening predicted hangover severity but FHP status did not. FHP, indicating familial risk for AUD, was not associated with concurrent hangover severity but was associated with increased estimates of hangover frequency the previous year