HEXACO personality factors as predictors of physical activity, resting heart rate, body mass index, and healthy lifestyle behaviors.

BACKGROUND: Personality traits are known factors that may influence levels of physical activity and other healthy lifestyle measures and behaviors that ultimately lead to health problems later in life. Participants And Procedure: The aim of this study was to examine the association between personality traits (HEXACO) and levels of physical activity and resting heart rate (RHR) - measured using Fitbits, BMI, and a self-reported whole-person healthy lifestyle score for N = 2580 college students. Data were collected and analyzed for students enrolled in a University Success type course from August 2017 to May 2021. The relationships between HEXACO personality traits and various physical activity and healthy lifestyle behaviors were analyzed by building several multiple regression models using R version 4.0.2. Results: In general, students who are extraverted were more physically active and students who are more open to experience had a higher RHR, even when controlling for gender. Females and males however had different profiles as to how personality influenced physical activity and other health-related measures. Male extraverts with high negative emotionality scores tend to be more physically active, whereas females tend to be more physically active when they were high in extroversion and conscientiousness, and low in openness to experience. BMI values were higher for female participants with high honesty-humility and low agreeableness and conscientiousness scores. Females also had a lower RHR for high honesty-humility and emotionality and low conscientiousness scores. CONCLUSIONS: Personality can influence levels of physical activity, RHR, and BMI. This is especially true of women. Being aware of one's personality and the relationship of personality traits to levels of physical activity and other measures of leading a healthy lifestyle can be beneficial in determining strategies to improve long-term health outcomes.

Approximating large-basis coupled-cluster theory vibrational frequencies using focal-point approximations.

The focal-point approximation can be used to estimate a high-accuracy, slow quantum chemistry computation by combining several lower-accuracy, faster computations. We examine the performance of focal-point methods by combining second-order Møller-Plesset perturbation theory (MP2) with coupled-cluster singles, doubles, and perturbative triples [CCSD(T)] for the calculation of harmonic frequencies and that of fundamental frequencies using second-order vibrational perturbation theory (VPT2). In contrast to standard CCSD(T), the focal-point CCSD(T) method approaches the complete basis set (CBS) limit with only triple-ζ basis sets for the coupled-cluster portion of the computation. The predicted harmonic and fundamental frequencies were compared with the experimental values for a set of 20 molecules containing up to six atoms. The focal-point method combining CCSD(T)/aug-cc-pV(T + d)Z with CBS-extrapolated MP2 has mean absolute errors vs experiment of only 7.3 cm-1 for the fundamental frequencies, which are essentially the same as the mean absolute error for CCSD(T) extrapolated to the CBS limit using the aug-cc-pV(Q + d)Z and aug-cc-pV(5 + d)Z basis sets. However, for H2O, the focal-point procedure requires only 3% of the computation time as the extrapolated CCSD(T) result, and the cost savings will grow for larger molecules.

Efficient design of neural networks for the classification of acoustic spectra.

A previous paper by Paul and Nelson [(2021). J. Acoust. Soc. Am. 149(6), 4119-4133] presented the application of the singular value decomposition (SVD) to the weight matrices of multilayer perceptron (MLP) networks as a pruning strategy to remove weight parameters. This work builds on the previous technique and presents a method of reducing the size of a hidden layer by applying a similar SVD algorithm. Results show that by reducing the neurons in the hidden layer, a significant amount of training time is saved compared to the algorithm presented in the previous paper while no or little accuracy is being lost compared to the original MLP model.

Development of a novel and more holistic approach for assessing impact in health and medical research: the Research Impact Assessment Framework.

Considered investment in health and medical research (HMR) is critical for fostering a healthcare system that is sustainable, effective, responsive, and innovative. While several tools exist to measure the impact of research, few assess the research environment that nurtures and supports impactful research and the strategic alignment of research with societal needs. This perspective article discusses the limitations of existing assessment tools and presents a novel Research Impact Assessment Framework designed to enable more strategic and targeted investment towards HMR, having the potential for significant public benefit.

The influence of chronotype on the body mass index of U.S. college students.

Objectives: The relationship between a college student's chronotype and body mass index (BMI) is important to understand for university decision makers who want to build healthy and inclusive academic communities. This study aimed to evaluate how a student's chronotype influences their BMI. Material and Methods: Participants were college students from Oral Roberts University (n=384) with a mean age of 18.94 years, a mean BMI of 24.7kg/m2, and a mean morningness-eveningness questionnaire (MEQ) score of 47.65. Results: BMI values were significantly correlated with both chronotype (r=-.11, ß=-.09, p=.03) and age (r=.12, ß=.53, p=.02). The rate at which BMI increased with age depended upon the student's chronotype (ß=.81-.005 / MEQ, p=.005). The later the chronotype, the higher the rate of increase. Race had no significant influence on MEQ or BMI values except in the case of students who identified as Black and female. These students were found, on average, to have significantly higher BMI values (p<.01). Conclusion: For college students, BMI tends to increase over time and at a rate that is dependent upon chronotype. The later the chronotype, the faster the rate at which BMI increases. BMI values were found to be significantly higher for Black females. However, this result is potentially spurious, as BMI does not take into account differences in body composition between genders and race/ethnicity groups.

Impact of the Radiation Oncology Alternative Payment Model on Brachytherapy Reimbursement.

PURPOSE: The Radiation Oncology Alternative Payment Model (RO Model) will test prospective radiotherapy episode-based payments for 16 common disease sites. We created an automated analytics platform to calculate the impact of the RO Model vs historical fee-for-service episode reimbursements for brachytherapy treatments within five community oncology practices for prostate, uterine, and cervical cancer. METHODS AND MATERIALS: Claims data between January 1, 2017 and October 2, 2019 for prostate, uterine, and cervical cancer were analyzed as per the RO Model Final Rule methodology. Expected professional and technical component (PC and TC) reimbursements were compared for episodes that utilized brachytherapy alone vs combination modality (external beam and brachytherapy) in the RO Model vs historical reimbursements. RESULTS: 6,022 RO Model-defined episodes (60% prostate, 28% uterine, 13% cervical) were generated. Brachytherapy monotherapy episodes (14%) would have an average positive reimbursement in the RO Model (+$2,163 for prostate, +$711 for uterine, +$533 for cervical for the PC; +$12,168 for prostate, +$8,181 for uterine, +$11,322 for cervical for the TC), while combination modality episodes (15%) would have an average negative reimbursement in the RO Model (-$183 for prostate, -$1,701 for uterine, -$2,195 for cervical for the PC; -$374 for prostate, -$5,026 for uterine, -$2,801 for cervical for the TC). CONCLUSIONS: Brachytherapy monotherapy episodes for prostate, uterine, and cervical cancer will benefit from an increase in payment, whereas combination modality episodes will receive lower reimbursement. Large shifts in episodic payment may be related to practice-wide adjustments and pricing based on partial episodes of care that may ultimately limit access to care for vulnerable patient populations with cancer.

Matrix analysis for fast learning of neural networks with application to the classification of acoustic spectra.

Neural networks are increasingly being applied to problems in acoustics and audio signal processing. Large audio datasets are being generated for use in training machine learning algorithms, and the reduction of training times is of increasing relevance. The work presented here begins by reformulating the analysis of the classical multilayer perceptron to show the explicit dependence of network parameters on the properties of the weight matrices in the network. This analysis then allows the application of the singular value decomposition (SVD) to the weight matrices. An algorithm is presented that makes use of regular applications of the SVD to progressively reduce the dimensionality of the network. This results in significant reductions in network training times of up to 50% with very little or no loss in accuracy. The use of the algorithm is demonstrated by applying it to a number of acoustical classification problems that help quantify the extent to which closely related spectra can be distinguished by machine learning.

Approaching "Elective" Surgery in the Era of COVID-19.

The coronavirus disease 2019 pandemic created unprecedented challenges for the health care system. To meet capacity demands, hospitals around the world suspended surgeries deemed to be elective. In hand surgery, numerous pathologies are treated on an elective basis, but a delay or absence of care may result in poorer outcomes. Here, we present an ethical framework for prioritizing elective surgery during a period of resource scarcity. Instead of using the term "elective," we define procedures that can be safely delayed on the basis of 3 considerations. First, a safe delay is possible only if deferral will not result in permanent injury. Second, a delay in care will come with tolerable costs and impositions that can be appropriately managed in the future. Third, a safe delay will preserve the bioethical principle of patient autonomy. In considering these criteria, 3 case examples are discussed considering individual patient characteristics and the pathophysiology of the condition. This framework design is applicable to ambulatory surgery in any period of crisis that may strain resources, but further considerations may be important if an operation requires hospital admission.

Hospice or Hospital: The Costs of Dying of Cancer in the Oncology Care Model.

Background: End-of-life management is a difficult aspect of cancer care. With the oncology care model (OCM), we have data to assess both clinical outcomes and total cost of care (TCOC). Objective: To measure and characterize the TCOC for those who received less than three days of hospice care (HC) at the end of life compared with those who received three days or more. Design: Assess data on costs and site and date of death from Medicare claims on patients identified in the OCM who received chemotherapy in the six months before death. Standard statistical methods were used to characterize both populations. Setting/Subjects: Subjects were Medicare patients with cancer who died while managed by U.S. oncology practices in the OCM. Measurements were TCOC in 30-day intervals for the last months of life, cost by site of care at the end of life, and demographic characteristics of the population and association with HC. Results: There were 7329 deaths. Dying in the hospital was twice the cost of dying at home under HC ($20,113 vs. $10,803). Of demographic groups measured, only black race and a lymphoma diagnosis had <50% hospice enrollment for three days or more before death. Conclusions: This study reinforces previous studies regarding costs in the last 30 days of life. The graphic representation highlights the dollar cost and the costs of lost opportunity. Using these data to improve communication, addressing socioeconomic support, and formal palliative care integration are potential strategies to improve care.

Applying Deep Neural Network Analysis to High-Content Image-Based Assays.

The etiological underpinnings of many CNS disorders are not well understood. This is likely due to the fact that individual diseases aggregate numerous pathological subtypes, each associated with a complex landscape of genetic risk factors. To overcome these challenges, researchers are integrating novel data types from numerous patients, including imaging studies capturing broadly applicable features from patient-derived materials. These datasets, when combined with machine learning, potentially hold the power to elucidate the subtle patterns that stratify patients by shared pathology. In this study, we interrogated whether high-content imaging of primary skin fibroblasts, using the Cell Painting method, could reveal disease-relevant information among patients. First, we showed that technical features such as batch/plate type, plate, and location within a plate lead to detectable nuisance signals, as revealed by a pre-trained deep neural network and analysis with deep image embeddings. Using a plate design and image acquisition strategy that accounts for these variables, we performed a pilot study with 12 healthy controls and 12 subjects affected by the severe genetic neurological disorder spinal muscular atrophy (SMA), and evaluated whether a convolutional neural network (CNN) generated using a subset of the cells could distinguish disease states on cells from the remaining unseen control-SMA pair. Our results indicate that these two populations could effectively be differentiated from one another and that model selectivity is insensitive to batch/plate type. One caveat is that the samples were also largely separated by source. These findings lay a foundation for how to conduct future studies exploring diseases with more complex genetic contributions and unknown subtypes.

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

Time to Stop Telling Biophysics Students that Light Is Primarily a Wave.

Standard pedagogy introduces optics as though it were a consequence of Maxwell's equations and only grudgingly admits, usually in a rushed aside, that light has a particulate character that can somehow be reconciled with the wave picture. Recent revolutionary advances in optical imaging, however, make this approach more and more unhelpful: How are we to describe two-photon imaging, FRET, localization microscopy, and a host of related techniques to students who think of light primarily as a wave? I was surprised to find that everything I wanted my biophysics students to know about light, including image formation, x-ray diffraction, and even Bessel beams, could be expressed as well (or better) from the quantum viewpoint pioneered by Richard Feynman. Even my undergraduate students grasp this viewpoint as well as (or better than) the traditional one, and by mid-semester they are already well positioned to integrate the latest advances into their understanding. Moreover, I have found that this approach clarifies my own understanding of new techniques.

Assessing microscope image focus quality with deep learning.

BACKGROUND: Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. RESULTS: We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. CONCLUSIONS: Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

The Role of Quantum Decoherence in FRET.

Resonance energy transfer has become an indispensable experimental tool for single-molecule and single-cell biophysics. Its physical underpinnings, however, are subtle: it involves a discrete jump of excitation from one molecule to another, and so we regard it as a strongly quantum-mechanical process. And yet its kinetics differ from what many of us were taught about two-state quantum systems, quantum superpositions of the states do not seem to arise, and so on. Although J. R. Oppenheimer and T. Förster navigated these subtleties successfully, it remains hard to find an elementary derivation in modern language. The key step involves acknowledging quantum decoherence. Appreciating that aspect can be helpful when we attempt to extend our understanding to situations in which Förster's original analysis is not applicable.

Sex Differences in Neuromuscular Fatigability of the Knee Extensors Post-Stroke.

BACKGROUND AND PURPOSE: Despite the implications of optimizing strength training post-stroke, little is known about the differences in fatigability between men and women with chronic stroke. The purpose of this study was to determine the sex differences in knee extensor muscle fatigability and potential mechanisms in individuals with stroke. METHODS: Eighteen participants (10 men, eight women) with chronic stroke (≥6 months) and 23 (12 men, 11 women) nonstroke controls participated in the study. Participants performed an intermittent isometric contraction task (6 s contraction, 3 s rest) at 30% of maximal voluntary contraction (MVC) torque until failure to maintain the target torque. Electromyography was used to determine muscle activation and contractile properties were assessed with electrical stimulation of the quadriceps muscles. RESULTS: Individuals with stroke had a briefer task duration (greater fatigability) than nonstroke individuals (24.1 ± 17 min vs. 34.9 ± 16 min). Men were more fatigable than women for both nonstroke controls and individuals with stroke (17.9 ± 9 min vs. 41.6 ± 15 min). Individuals with stroke had less fatigue-related changes in muscle contractile properties and women with stroke differed in their muscle activation strategy during the fatiguing contractions. CONCLUSIONS: Men and women fatigue differently post-stroke and this may be due to the way they neurally activate muscle groups.

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. Design and Setting: A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. Exposure: Deep learning-trained algorithm. Main Outcomes and Measures: The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. Results: The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%. Conclusions and Relevance: In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.

Old and new results about single-photon sensitivity in human vision.

It is sometimes said that 'our eyes can see single photons'. This article begins by finding a more precise version of that claim and reviewing evidence gathered for it up to around 1985 in two distinct realms, those of human psychophysics and single-cell physiology. Finding a single framework that accommodates both kinds of result is then a nontrivial challenge, and one that sets severe quantitative constraints on any model of dim-light visual processing. This article presents one such model and compares it to a recent experiment.

Differential effectiveness of group, individual, and conjoint treatments: An archival analysis of OQ-45 change trajectories.

OBJECTIVE: While empirically-supported treatment (EST) choices are continually expanding, choices regarding formats for delivery (individual only, group only, or conjoint [simultaneous individual & group]) are often determined by agency resources or clinician preference. Studies comparing individual and group formats have produced mixed results, while recent meta-analytic reviews support format equivalence. METHOD: We employed a multilevel model to test for outcome differences using the OQ-45 on an outpatient archival data set of clients receiving individual-only (n = 11,764), group-only (n = 152) or conjoint (n = 1557). RESULTS: Individual and group outcomes were equivalent with some analyses showing conjoint trailing. Moderators of change included initial distress, treatment duration, intra-group dependency, and format. CONCLUSIONS: Results support meta-analytic findings of format equivalence in a naturalistic setting for group and individual. Referral practices and future results are discussed.

Engineering practice variation through provider agreement: a cluster-randomized feasibility trial.

PURPOSE: Minimal-risk randomized trials that can be embedded in practice could facilitate learning health-care systems. A cluster-randomized design was proposed to compare treatment strategies by assigning clusters (eg, providers) to "favor" a particular drug, with providers retaining autonomy for specific patients. Patient informed consent might be waived, broadening inclusion. However, it is not known if providers will adhere to the assignment or whether institutional review boards will waive consent. We evaluated the feasibility of this trial design. SUBJECTS AND METHODS: Agreeable providers were randomized to "favor" either hydrochlorothiazide or chlorthalidone when starting patients on thiazide-type therapy for hypertension. The assignment applied when the provider had already decided to start a thiazide, and providers could deviate from the strategy as needed. Prescriptions were aggregated to produce a provider strategy-adherence rate. RESULTS: All four institutional review boards waived documentation of patient consent. Providers (n=18) followed their assigned strategy for most of their new thiazide prescriptions (n=138 patients). In the "favor hydrochlorothiazide" group, there was 99% adherence to that strategy. In the "favor chlorthalidone" group, chlorthalidone comprised 77% of new thiazide starts, up from 1% in the pre-study period. When the assigned strategy was followed, dosing in the recommended range was 48% for hydrochlorothiazide (25-50 mg/day) and 100% for chlorthalidone (12.5-25.0 mg/day). Providers were motivated to participate by a desire to contribute to a comparative effectiveness study. A study promotional mug, provider information letter, and interactions with the site investigator were identified as most helpful in reminding providers of their study drug strategy. CONCLUSION: Providers prescribed according to an assigned drug-choice strategy most of the time for the purpose of a comparative effectiveness study. This simple design could facilitate research participation and behavior change in non-research clinicians. Waiver of patient consent can broaden the representation of patients, providers, and settings.