An argument that individuals and collectives form memories by analogous processes and a case study of collective retrograde amnesia.
We form individual memories by a process known as consolidation: ...the conversion of immediate and fleeting bits of information into a stable and accessible representation of facts and events. These memories provide a version of the past that helps us navigate the present and is critical to individual identity. In this book, Thomas Anastasio, Kristen Ann Ehrenberger, Patrick Watson, and Wenyi Zhang propose that social groups form collective memories by analogous processes. Using facts and insights from neuroscience, psychology, anthropology, and history, they describe a single process of consolidation with analogous—not merely comparable—manifestations on any level, whether brain, family, or society. They propose a three-in-one model of memory consolidation, composed of a buffer, a relator, and a generalizer, all within the consolidating entity, that can explain memory consolidation phenomena on individual and collective levels.
When consolidation is disrupted by traumatic injury to a brain structure known as the hippocampus, memories in the process of being consolidated are lost. In individuals, this is known as retrograde amnesia. The authors hypothesize a "social hippocampus" and argue that disruption at the collective level can result in collective retrograde amnesia. They offer the Chinese Cultural Revolution (1966–1976) as an example of trauma to the social hippocampus and present evidence for the loss of recent collective memory in mainland Chinese populations that experienced the Cultural Revolution.
Introduction The use of biologic adjuvants (orthobiologics) is becoming commonplace in orthopaedic surgery. Among other applications, biologics are often added to enhance fusion rates in spinal ...surgery and to promote bone healing in complex fracture patterns. Generally, orthopaedic surgeons use only one biomolecular agent (ie allograft with embedded bone morphogenic protein-2) rather than several agents acting in concert. Bone fusion, however, is a highly multifactorial process and it likely could be more effectively enhanced using biologic factors in combination, acting synergistically. We used artificial neural networks, trained via machine learning on experimental data on orthobiologic interventions and their outcomes, to identify combinations of orthobiologic factors that potentially would be more effective than single agents. This use of machine learning applied to orthobiologic interventions is unprecedented. Methods Available data on the outcomes associated with various orthopaedic biologic agents, electrical stimulation, and pulsed ultrasound were curated from the literature and assembled into a form suitable for machine learning. The best among many different types of neural networks was chosen for its ability to generalize over this dataset, and that network was used to make predictions concerning the expected efficacy of 2400 medically feasible combinations of 9 different agents and treatments. Results The most effective combinations were high in the bone-morphogenic proteins (BMP) 2 and 7 (BMP2, 15mg; BMP7, 5mg), and in osteogenin (150ug). In some of the most effective combinations, electrical stimulation could substitute for osteogenin. Some other effective combinations also included bone marrow aspirate concentrate. BMP2 and BMP7 appear to have the strongest pairwise linkage of the factors analyzed in this study. Conclusions Artificial neural networks are powerful forms of artificial intelligence that can be applied readily in the orthopaedic domain, but neural network predictions improve along with the amount of data available to train them. This study provides a starting point from which networks trained on future, expanded datasets can be developed. Yet even this initial model makes specific predictions concerning potentially effective combinatorial therapeutics that should be verified experimentally. Furthermore, our analysis provides an avenue for further research into the basic science of bone healing by demonstrating agents that appear to be linked in function.
The Church lives not for herself. She offers herself for the whole of humanity in order to raise up and renew the world into new heavens and a new earth (cf. Rev 21.1). Hence, she gives Gospel ...witness and distributes the gifts of God in the world: His love, peace, justice, reconciliation, the power of the Resurrection and the expectation of eternal life.
The rate of consumption of dithiothreitol (DTT) is increasingly used to measure the oxidative potential of particulate matter (PM), which has been linked to the adverse health effects of PM. While ...several quinones are known to be very reactive in the DTT assay, it is unclear what other chemical species might contribute to the loss of DTT in PM extracts. To address this question, we quantify the rate of DTT loss from individual redox-active species that are common in ambient particulate matter. While most past research has indicated that the DTT assay is not sensitive to metals, our results show that seven out of the ten transition metals tested do oxidize DTT, as do three out of the five quinones tested. While metals are less efficient at oxidizing DTT compared to the most reactive quinones, concentrations of soluble transition metals in fine particulate matter are generally much higher than those of quinones. The net result is that metals appear to dominate the DTT response for typical ambient PM2.5 samples. Based on particulate concentrations of quinones and soluble metals from the literature, and our measured DTT responses for these species, we estimate that for typical PM2.5 samples approximately 80% of DTT loss is from transition metals (especially copper and manganese), while quinones account for approximately 20%. We find a similar result for DTT loss measured in a small set of PM2.5 samples from the San Joaquin Valley of California. Because of the important contribution from metals, we also tested how the DTT assay is affected by EDTA, a chelator that is sometimes used in the assay. EDTA significantly suppresses the response from both metals and quinones; we therefore recommend that EDTA should not be included in the DTT assay.
Purpose: Outpatient classified total hip arthroplasty (THA) is a safe option for a select group of patients. An analysis of a national database was conducted to understand the risk factors for ...unplanned discharge to a skilled nursing facility (SNF) or acute rehabilitation (rehab) after outpatient classified THA.
Materials and Methods: A query of the National Surgical Quality Improvement Program (NSQIP) database for THA (Current Procedural Terminology CPT 27130) performed from 2015 to 2018 was conducted. Patient demographics, American Society of Anesthesiologists (ASA) classification, functional status, NSQIP morbidity probability, operative time, length of stay (LOS), 30-day reoperation rate, readmission rate, and associated complications were collected.
Results: A total of 2,896 patients underwent outpatient classified THA. The mean age of patients was 61.2 years. The mean body mass index (BMI) was 29.6 kg/m2 with median ASA 2. The results of univariate comparison of SNF/rehab versus home discharge showed that a significantly higher percentage of females (58.7% vs. 46.8%), age >70 years (49.3% vs. 20.9%), ASA ≥3 (58.0% vs. 25.8%), BMI >35 kg/m2 (23.3% vs. 16.2%), and hypoalbuminemia (8.0% vs. 1.5%) (P< 0.0001) were discharged to SNF/rehab. The results of multivariable logistic regression showed that female sex (odds ratio OR 1.47; P=0.03), age >70 years (OR 3.08; P=0.001), ASA≥3 (OR 2.56; P=0.001), and preoperative hypoalbuminemia (<3.5 g/dL) (OR 3.76; P=0.001) were independent risk factors for SNF/rehab discharge.
Conclusion: Risk factors associated with discharge to a SNF/rehab after outpatient classified THA were identified. Surgeons will be able to perform better risk stratification for patients who may require additional postoperative intervention.
It is widely accepted that the optimization of medical imaging system performance should be guided by task-based measures of image quality (IQ). Task-based measures of IQ quantify the ability of an ...observer to perform a specific task, such as detection or estimation of a signal (e.g., a tumor). For binary signal detection tasks, the Bayesian Ideal Observer (IO) sets an upper limit of observer performance and has been advocated for use in optimizing medical imaging systems and data-acquisition designs. Except in special cases, the determination of the IO test statistic is analytically intractable. Markov-chain Monte Carlo (MCMC) techniques can be employed to approximate the IO detection performance, but their reported applications have been limited to relatively simple object models. In cases where the IO test statistic is difficult to compute, the Hotelling Observer (HO) can be employed. To compute the HO test statistic, potentially large covariance matrices must be accurately estimated and subsequently inverted, which can present computational challenges. This paper investigates the supervised learning-based methodologies for approximating the IO and HO test statistics. Convolutional neural networks (CNNs) and single-layer neural networks (SLNNs) are employed to approximate the IO and HO test statistics, respectively. The numerical simulations were conducted for both signal-known-exactly (SKE) and signal-known-statistically (SKS) signal detection tasks. The considered background models include the lumpy object model and the clustered lumpy object model. The measurement noise models considered are Gaussian, Laplacian, and mixed Poisson-Gaussian. The performances of the supervised learning methods are assessed via receiver operating characteristic (ROC) analysis, and the results are compared to those produced by the use of traditional numerical methods or analytical calculations when feasible. The potential advantages of the proposed supervised learning approaches for approximating the IO and HO test statistics are discussed.
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