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.

Broadening access to small-molecule parameterization with the force field toolkit.

Access to accurate force-field parameters for small molecules is crucial for computational studies of their interactions with proteins. Although a number of general force fields for small molecules exist, e.g., CGenFF, GAFF, and OPLS, they do not cover all common chemical groups and their combinations. The Force Field Toolkit (ffTK) provides a comprehensive graphical interface that streamlines the development of classical parameters for small molecules directly from quantum mechanical (QM) calculations, allowing for force-field generation for almost any chemical group and validation of the fit relative to the target data. ffTK relies on supported external software for the QM calculations, but it can generate the necessary QM input files and parse and analyze the QM output. In previous ffTK versions, support for Gaussian and ORCA QM packages was implemented. Here, we add support for Psi4, an open-source QM package free for all users, thereby broadening user access to ffTK. We also compare the parameter sets obtained with the new ffTK version using Gaussian, ORCA, and Psi4 for three molecules: pyrrolidine, n-propylammonium cation, and chlorobenzene. Despite minor differences between the resulting parameter sets for each compound, most prominently in the dihedral and improper terms, we show that conformational distributions sampled in molecular dynamics simulations using these parameter sets are quite comparable.

Efficient design of complex-valued neural networks with application to the classification of transient acoustic signals.

A paper by the current authors Paul and Nelson [JASA Express Lett. 3(9), 094802 (2023)] showed how the singular value decomposition (SVD) of the matrix of real weights in a neural network could be used to prune the network during training. The paper presented here shows that a similar approach can be used to reduce the training time and increase the implementation efficiency of complex-valued neural networks. Such networks have potential advantages compared to their real-valued counterparts, especially when the complex representation of the data is important, which is the often case in acoustic signal processing. In comparing the performance of networks having both real and complex elements, it is demonstrated that there are some advantages to the use of complex networks in the cases considered. The paper includes a derivation of the backpropagation algorithm, in matrix form, for training a complex-valued multilayer perceptron with an arbitrary number of layers. The matrix-based analysis enables the application of the SVD to the complex weight matrices in the network. The SVD-based pruning technique is applied to the problem of the classification of transient acoustic signals. It is shown how training times can be reduced, and implementation efficiency increased, while ensuring that such signals can be classified with remarkable accuracy.

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.

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.

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.

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.

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.

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.

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.

Scientific basis for safely shutting in the Macondo Well after the April 20, 2010 Deepwater Horizon blowout.

As part of the government response to the Deepwater Horizon blowout, a Well Integrity Team evaluated the geologic hazards of shutting in the Macondo Well at the seafloor and determined the conditions under which it could safely be undertaken. Of particular concern was the possibility that, under the anticipated high shut-in pressures, oil could leak out of the well casing below the seafloor. Such a leak could lead to new geologic pathways for hydrocarbon release to the Gulf of Mexico. Evaluating this hazard required analyses of 2D and 3D seismic surveys, seafloor bathymetry, sediment properties, geophysical well logs, and drilling data to assess the geological, hydrological, and geomechanical conditions around the Macondo Well. After the well was successfully capped and shut in on July 15, 2010, a variety of monitoring activities were used to assess subsurface well integrity. These activities included acquisition of wellhead pressure data, marine multichannel seismic profiles, seafloor and water-column sonar surveys, and wellhead visual/acoustic monitoring. These data showed that the Macondo Well was not leaking after shut in, and therefore, it could remain safely shut until reservoir pressures were suppressed (killed) with heavy drilling mud and the well was sealed with cement.

Transformation of stimulus correlations by the retina.

Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible.

Single-Molecule Studies of Cognate and Near-Cognate Elongation in an in vitro Eukaryotic Translation System.

The ribosome plays a central role in translation of the genetic code into amino acid sequences during synthesis of polypeptides. During each cycle of peptide elongation, the ribosome must discriminate between correct and incorrect aminoacyl-tRNAs according to the codon present in its A-site. Ribosomes rely on a complex sequence of proofreading mechanisms to minimize erroneous selection of incorrect aminoacyl-tRNAs that would lead to mistakes in translation. These mechanisms have been studied extensively in prokaryotic organisms, but eukaryotic elongation is less well understood. Here, we use single-molecule fluorescence resonance energy transfer (smFRET) with an in vitro eukaryotic translation system to investigate tRNA selection and subsequent steps during peptide elongation. We compared accommodation of a tryptophan-aminoacyl-tRNA into the ribosomal A-site containing either a cognate or near-cognate codon and unexpectedly found that, following an initial slow sampling event, subsequent near-cognate sampling events proceeded more rapidly than the initial event. Further, we found a strong negative correlation between the concentration of near-cognate aminoacyl-tRNA and the efficiency of tRNA accommodation. These novel characteristics of near-cognate interaction with the eukaryotic ribosome suggest that rejection of a near-cognate tRNAs leads to formation of an altered ribosomal conformation that assists in rejecting subsequent incorrect tRNA interactions.

Association of Accelerometer-Measured Physical Activity With College Student Success and Retention.

BACKGROUND: This study examined the relationship between physical activity (PA) and academic performance and retention among college students using accelerometer data while controlling for sex and socioeconomic background. METHODS: Data were collected from 4643 first-year college students at a private university in the south-central United States who began their studies in the Fall semesters between 2015 and 2022. Daily step counts were collected using accelerometers worn as part of the institutions PA requirements. The expected family contribution was calculated based on information provided on the Free Application for Federal Student Aid. Grade point average (GPA) and retention data were collected directly from official university databases. RESULTS: Female students generally had lower median age and steps count and a higher median GPA than males. The retention rates from fall to spring and fall to fall were 95.9% and 83.8%, respectively, with no significant difference in retention rates between males and females. GPA was significantly positively correlated with steps, expected family contribution, and age, and negatively correlated with being male and having an expected family contribution of zero. The fall to spring retention rate was significantly positively correlated with steps and GPA. CONCLUSIONS: The study's findings suggest that increasing levels of PA, as measured by daily step counts, may be effective in promoting academic performance and retention, even when controlling for sex and socioeconomic background. The use of device-based measures of PA in this study contributes to the literature on this topic, and policymakers and educational institutions should consider interventions focused on academic performance and physical activity to help students persist.

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.

Tilting and wobble of myosin V by high-speed single-molecule polarized fluorescence microscopy.

Myosin V is biomolecular motor with two actin-binding domains (heads) that take multiple steps along actin by a hand-over-hand mechanism. We used high-speed polarized total internal reflection fluorescence (polTIRF) microscopy to study the structural dynamics of single myosin V molecules that had been labeled with bifunctional rhodamine linked to one of the calmodulins along the lever arm. With the use of time-correlated single-photon counting technology, the temporal resolution of the polTIRF microscope was improved ~50-fold relative to earlier studies, and a maximum-likelihood, multitrace change-point algorithm was used to objectively determine the times when structural changes occurred. Short-lived substeps that displayed an abrupt increase in rotational mobility were detected during stepping, likely corresponding to random thermal fluctuations of the stepping head while it searched for its next actin-binding site. Thus, myosin V harnesses its fluctuating environment to extend its reach. Additional, less frequent angle changes, probably not directly associated with steps, were detected in both leading and trailing heads. The high-speed polTIRF method and change-point analysis may be applicable to single-molecule studies of other biological systems.

Sound field reproduction as an equivalent acoustical scattering problem.

Given a continuous distribution of acoustic sources, the determination of the source strength that ensures the synthesis of a desired sound field is shown to be identical to the solution of an equivalent acoustic scattering problem. The paper begins with the presentation of the general theory that underpins sound field reproduction with secondary sources continuously arranged on the boundary of the reproduction region. The process of reproduction by a continuous source distribution is modeled by means of an integral operator (the single layer potential). It is then shown how the solution of the sound reproduction problem corresponds to that of an equivalent scattering problem. Analytical solutions are computed for two specific instances of this problem, involving, respectively, the use of a secondary source distribution in spherical and planar geometries. The results are shown to be the same as those obtained with analyses based on High Order Ambisonics and Wave Field Synthesis, respectively, thus bringing to light a fundamental analogy between these two methods of sound reproduction. Finally, it is shown how the physical optics (Kirchhoff) approximation enables the derivation of a high-frequency simplification for the problem under consideration, this in turn being related to the secondary source selection criterion reported in the literature on Wave Field Synthesis.

Youth psychotherapy change trajectories and early warning system accuracy in a managed care setting.

OBJECTIVES: To examine change trajectories in routine outpatient mental health services for children and adolescents in a managed care setting, and to use these trajectories to test the accuracy of two variations of an early warning system designed to identify cases at risk for deterioration. METHOD: Multilevel modeling procedures were used to examine longitudinal Youth Outcome Questionnaire (YOQ) data for 16,091 youth aged 4-17 years (39% female, mean age 10.5) referred for treatment in a managed care system. RESULTS: Clients with more frequent YOQ administrations had slightly lower baselines and faster rates of change. Both the traditional and simplified versions of the early warning system demonstrated good accuracy in identifying clients who deteriorated, with a sensitivity of .63, specificity of .83, and hit rate of .81. CONCLUSIONS: Results provide further evidence that patient-focused early warning systems can accurately identify most youth who are at risk for negative outcomes in routine mental health services.