Search

BackgroundIn this paper, we predict the health and economic consequences of immediate investment in personal protective equipment (PPE) for health care workers (HCWs) in low- and middle-income countries (LMICs).MethodsTo account for health consequences, we estimated mortality for health care workers (HCW), and present a cost-effectiveness and return on investment (ROI) analysis using a decision-analytic model with Bayesian multivariate sensitivity analysis and Monte Carlo simulation. Inputs were used from the World Health Organization Essential Supplies Forecasting Tool and the Imperial College of London epidemiologic model.ResultsAn investment of $9.6 billion USD would adequately protect HCWs in all LMICs. This intervention saves 2,299,543 lives across LMICs, costing $59 USD per HCW case averted and $4,309 USD per HCW life saved. The societal ROI is $755.3 billion USD, the equivalent of a 7,932% return. Regional and national estimates are also presented. In scenarios where PPE remains scarce, 70-100% of HCWs will get infected, irrespective of nationwide social distancing policies. Maintaining HCW infection rates below 10% and mortality below 1% requires inclusion of a PPE scale-up strategy as part of the pandemic response.DiscussionIn conclusion, wide-scale procurement and distribution of PPE for LMICs is an essential strategy to prevent widespread HCW morbidity and mortality. It is cost-effective and yields a large downstream return on investment.

With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these images. One of the challenges in providing such methodology is the need for tailoring algorithms to the specifics of the data, limiting their areas of application. Here we present a broadly applicable approach to quantification and classification of complex shapes and patterns in biological or other multi-component formations. This approach integrates the mapping of all shape boundaries within an image onto a global information-rich graph and machine learning on the multidimensional measures of the graph. We demonstrated the power of this method by (1) extracting subtle structural differences from visually indistinguishable images in our phenotype rescue experiments using the endothelial tube formations assay, (2) training the algorithm to identify biophysical parameters underlying the formation of different multicellular networks in our simulation model of collective cell behavior, and (3) analyzing the response of U2OS cell cultures to a broad array of small molecule perturbations., Author summary In this paper, we present a methodology that is based on mapping an arbitrary set of outlines onto a complete, strictly defined structure, in which every point representing the shape becomes a terminal point of a global graph. Because this mapping preserves the whole complexity of the shape, it allows for extracting the full scope of geometric features of any scale. Importantly, an extensive set of graph-based metrics in each image makes integration with machine learning routines highly efficient even for a small data sets and provide an opportunity to backtrack the subtle morphological features responsible for the automated distinction into image classes. The resulting tool provides efficient, versatile, and robust quantification of complex shapes and patterns in experimental images.

Statistical learning and probabilistic prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n-grams from training sequences. However, PPM has limitations as a cognitive model: in particular, it has a perfect memory that weights all historic observations equally, which is inconsistent with memory capacity constraints and recency effects observed in human cognition. We address these limitations with PPM-Decay, a new variant of PPM that introduces a customizable memory decay kernel. In three studies—one with artificially generated sequences, one with chord sequences from Western music, and one with new behavioral data from an auditory pattern detection experiment—we show how this decay kernel improves the model’s predictive performance for sequences whose underlying statistics change over time, and enables the model to capture effects of memory constraints on auditory pattern detection. The resulting model is available in our new open-source R package, ppm (https://github.com/pmcharrison/ppm)., Author summary Humans hear a rich variety of sounds throughout everyday life, ranging from the basic (e.g. an alarm clock, a whistling kettle, an ambulance siren) to the complex (e.g. speech, music, birdsong). Understanding these sounds depends on an ability to detect and remember patterns in these sounds, patterns ranging from the two-tone oscillation of the ambulance siren to the classic four-chord progression of Western popular music. A key challenge in audition research is to develop effective computer models of these pattern-detection processes. The Prediction by Partial Matching model is one such model, originally developed for data compression, that learns statistical patterns of varying complexity from sequences of discrete symbols (e.g. ‘A, B, A, A, B, A, B’). In previous research this model has proved particularly effective for simulating listeners’ responses to music as well as other kinds of auditory sequences. However, the model is an unrealistic simulation of human cognition in that it possesses a perfect memory, unbounded in capacity, where historic events are recalled just as clearly as recent events. In this paper we therefore introduce a memory-decay component to the model, whereby the salience of historic auditory events decreases over time in line with the dynamics of human auditory memory. We present an experiment showing that this memory-decay model provides a natural account of experimental data from an auditory pattern detection task, explaining how human performance deteriorates as a function of the length of the patterns being detected and the speed at which they are played. Conversely, we also present two simulation studies showing that this memory-decay component can improve pattern detection in auditory environments whose statistical structure changes dynamically over time. These studies indicate the potential benefit of incorporating memory constraints into statistical models of auditory pattern detection, and highlight how these memory constraints can both impair and improve pattern detection, depending on the environment.

Literature mentions two types of models describing cyclic movement-theory and data driven. Theory driven models include anatomical and physiological aspects. They are principally suitable for answering questions about the reasons for movement characteristics, but they are complicated and substantial simplifications do not allow generally valid results. Data driven models allow answering specific questions, but lack the understanding of the general movement characteristic. With this paper we try a compromise without having to rely on anatomy, neurology and muscle function. We hypothesize a general kinematic description of cyclic human motion is possible without having to specify the movement generating processes, and still get the kinematics right. The model proposed consists of a superposition of six contributions-subject's attractor, morphing, short time fluctuation, transient effect, control mechanism and sensor noise, while characterizing numbers and random contributions. We test the model with data from treadmill running and stationary biking. Applying the model in a simulation results in good agreement between measured data and simulation values. We find in all our cases the similarity analysis between measurement and simulation is best for the same subjects-[Formula: see text] and [Formula: see text]. All comparisons between different subjects are [Formula: see text] and [Formula: see text]. This uniquely allows for the identification of each measurement for the associated simulation. However, even different subject comparisons show good agreement between measurement and simulation results of differences δrun = 6.7±4.7% and δbike = 5.1±4.5%. published

Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their B cell receptor (BCR) sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse junction region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the AGPLv3 license., Author summary B cells recognize antigens through their BCR. During adaptive immune responses, antigen-specific B cells undergo intense proliferation. This B cell clonal expansion is coupled with a process of SHM, which results in the accumulation of mutations in the DNA encoding the BCR. Within the specialized micro-environment of the germinal center, these diversified B cells compete for antigen binding and presentation to follicular helper T cells. Successful binding leads to repeated cycles of proliferation, SHM and affinity-dependent selection ultimately resulting in the generation of high-affinity memory and antibody-secreting plasma cells. Driven by dramatic improvements in high-throughput sequencing technologies, large-scale characterization of BCR repertoires is now feasible. However, a critical barrier to quantitative analysis of these large-scale BCR repertoire data is the accurate identification of B cell clones. B cells are inferred to be clonally related if the distance between their BCR sequences is close enough. This paper develops a hybrid distance function that integrates information from the V(D)J recombination process (distance between CDR3 sequences), along with information from a common history of clonal expansion (shared SHMs in the V and J segments of the BCR) to improve the ability to identify clonally related sequences.

Advanced imaging techniques generate large datasets capable of describing the structure and kinematics of tissue spreading in embryonic development, wound healing, and the progression of many diseases. These datasets can be integrated with mathematical models to infer biomechanical properties of the system, typically identifying an optimal set of parameters for an individual experiment. However, these methods offer little information on the robustness of the fit and are generally ill-suited for statistical tests of multiple experiments. To overcome this limitation and enable efficient use of large datasets in a rigorous experimental design, we use the approximate Bayesian computation rejection algorithm to construct probability density distributions that estimate model parameters for a defined theoretical model and set of experimental data. Here, we demonstrate this method with a 2D Eulerian continuum mechanical model of spreading embryonic tissue. The model is tightly integrated with quantitative image analysis of different sized embryonic tissue explants spreading on extracellular matrix (ECM) and is regulated by a small set of parameters including forces on the free edge, tissue stiffness, strength of cell-ECM adhesions, and active cell shape changes. We find statistically significant trends in key parameters that vary with initial size of the explant, e.g., for larger explants cell-ECM adhesion forces are weaker and free edge forces are stronger. Furthermore, we demonstrate that estimated parameters for one explant can be used to predict the behavior of other similarly sized explants. These predictive methods can be used to guide further experiments to better understand how collective cell migration is regulated during development.Author SummaryNew imaging tools and automated microscopes are able to produce terabytes of detailed images of protein activity and cell movements as tissues change shape and grow. Yet, efforts to infer useful quantitative information from these large datasets have been limited by the inability to integrate image analysis and computational models with rigorous statistical methods. In this paper, we describe a robust methodology for inferring mechanical processes that drive tissue spreading in embryonic development. Tissue spreading is critical during wound healing and the progression of many diseases including cancer. Direct measurement of biomechanical properties during spreading is not possible in many cases, but can be inferred through mathematical and statistical means. This approach identifies model parameters that are able to robustly predict results of new experiments. These methods can be integrated with more general studies of morphogenesis and to guide further experiments to better understand how tissue spreading is regulated during development and potentially control spreading during wound healing and cancer.