Although motor learning is likely to involve multiple processes, phenomena observed in error-based motor learning paradigms tend to be conceptualized in terms of only a single process: adaptation, ...which occurs through updating an internal model. Here we argue that fundamental phenomena like movement direction biases, savings (faster relearning), and interference do not relate to adaptation but instead are attributable to two additional learning processes that can be characterized as model-free: use-dependent plasticity and operant reinforcement. Although usually “hidden” behind adaptation, we demonstrate, with modified visuomotor rotation paradigms, that these distinct model-based and model-free processes combine to learn an error-based motor task. (1) Adaptation of an internal model channels movements toward successful error reduction in visual space. (2) Repetition of the newly adapted movement induces directional biases toward the repeated movement. (3) Operant reinforcement through association of the adapted movement with successful error reduction is responsible for savings.
► Internal models are not sufficient to explain results in adaptation paradigms ► Repetition of adapted movements leads to use-dependent movement biases ► Operant association between adapted movements and success enables savings ► Motor tasks are learned by combining model-based and model-free processes
Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact ...on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols. We first describe current knowledge of the natural history of arm recovery after stroke and of outcome prediction in individual patients. Rehabilitation strategies and outcome measures for impairment versus function are compared. The topics of dosage, intensity, and time of rehabilitation are then discussed. Robots are particularly suitable for both rigorous testing and application of motor learning principles to neurorehabilitation. Computational motor control and learning principles derived from studies in healthy subjects are introduced in the context of robotic neurorehabilitation. Particular attention is paid to the idea of context, task generalization and training schedule. The assumptions that underlie the choice of both movement trajectory programmed into the robot and the degree of active participation required by subjects are examined. We consider rehabilitation as a general learning problem, and examine it from the perspective of theoretical learning frameworks such as supervised and unsupervised learning. We discuss the limitations of current robotic neurorehabilitation paradigms and suggest new research directions from the perspective of computational motor learning.
Holistic interventions to overcome COVID-19 vaccine hesitancy require a system-level understanding of the interconnected causes and mechanisms that give rise to it. However, conventional correlative ...analyses do not easily provide such nuanced insights. We used an unsupervised, hypothesis-free causal discovery algorithm to learn the interconnected causal pathways to vaccine intention as a causal Bayesian network (BN), using data from a COVID-19 vaccine hesitancy survey in the US in early 2021. We identified social responsibility, vaccine safety and anticipated regret as prime candidates for interventions and revealed a complex network of variables that mediate their influences. Social responsibility's causal effect greatly exceeded that of other variables. The BN revealed that the causal impact of political affiliations was weak compared with more direct causal factors. This approach provides clearer targets for intervention than regression, suggesting it can be an effective way to explore multiple causal pathways of complex behavioural problems to inform interventions.
Mobility after spinal cord injury (SCI) is among the top goals of recovery and improvement in quality of life. Those with tetraplegia rank hand function as the most important area of recovery in ...their lives, and those with paraplegia, walking. Without hand function, emphasis in rehabilitation is placed on accessing one’s environment through technology. However, there is still much reliance on caretakers for many activities of daily living. For those with paraplegia, if incomplete, orthoses exist to augment walking function, but they require a significant amount of baseline strength and significant energy expenditure to use. Options for those with motor complete paraplegia have traditionally been limited to the wheelchair. While wheelchairs provide a modified level of independence, wheelchair users continue to face difficulties in access and mobility. In the past decade, research in SCI rehabilitation has expanded to include external motorized or robotic devices that initiate or augment movement. These robotic devices are used with 2 goals: to enhance recovery through repetitive, functional movement and increased neural plasticity and to act as a mobility aid beyond orthoses and wheelchairs. In addition, lower extremity exoskeletons have been shown to provide benefits to the secondary medical conditions after SCI such as pain, spasticity, decreased bone density, and neurogenic bowel. In this review, we discuss advances in robot-guided rehabilitation after SCI for the upper and lower extremities, as well as potential adjuncts to robotics.
Here we demonstrate association of variants in the mitochondrial asparaginyl-tRNA synthetase NARS2 with human hearing loss and Leigh syndrome. A homozygous missense mutation (c.637G>T; p.Val213Phe) ...is the underlying cause of nonsyndromic hearing loss (DFNB94) and compound heterozygous mutations (c.969T>A; p.Tyr323* + c.1142A>G; p.Asn381Ser) result in mitochondrial respiratory chain deficiency and Leigh syndrome, which is a neurodegenerative disease characterized by symmetric, bilateral lesions in the basal ganglia, thalamus, and brain stem. The severity of the genetic lesions and their effects on NARS2 protein structure cosegregate with the phenotype. A hypothetical truncated NARS2 protein, secondary to the Leigh syndrome mutation p.Tyr323* is not detectable and p.Asn381Ser further decreases NARS2 protein levels in patient fibroblasts. p.Asn381Ser also disrupts dimerization of NARS2, while the hearing loss p.Val213Phe variant has no effect on NARS2 oligomerization. Additionally we demonstrate decreased steady-state levels of mt-tRNAAsn in fibroblasts from the Leigh syndrome patients. In these cells we show that a decrease in oxygen consumption rates (OCR) and electron transport chain (ETC) activity can be rescued by overexpression of wild type NARS2. However, overexpression of the hearing loss associated p.Val213Phe mutant protein in these fibroblasts cannot complement the OCR and ETC defects. Our findings establish lesions in NARS2 as a new cause for nonsyndromic hearing loss and Leigh syndrome.
The
LauePt
program is a popular and easy-to-use crystallography tool for indexing and simulating X-ray Laue patterns, but its previous versions lack search functions for recognizing Laue patterns ...taken from crystals with unknown orientations. To overcome this obstacle, a major upgrade of the program, called
LauePt4
, is presented with three robust search schemes implemented: (i) crystal rotation along a single diffraction vector, (ii) a look-up method to search for reflection pairs matching the interplanar angle of two selected diffraction spots, and (iii) a more efficient look-up scheme to search for reflection triplets matching three interplanar angles. Extensive tests show that all these schemes, together with the convenient graphical user interfaces and highly optimized computing algorithms, are reliable and powerful for recognizing and fitting Laue patterns of any crystal taken under any diffraction geometry.
The COVID-19 pandemic has impacted people's lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. ...Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States.
We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with-or precede-real-life events?
We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states.
The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others.
COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.
The third-generation EGFR inhibitor, osimertinib, is the first mutant-selective inhibitor that has received regulatory approval for the treatment of patients with
-mutant lung cancer. Despite the ...development of highly selective third-generation inhibitors, acquired resistance remains a significant clinical challenge. Recently, we and others have identified a novel osimertinib resistance mutation, G724S, which was not predicted in
screens. Here, we investigate how G724S confers resistance to osimertinib.
We combine structure-based predictive modeling of G724S in combination with the 2 most common EGFR-activating mutations, exon 19 deletion (Ex19Del) and L858R, with
drug-response models and patient genomic profiling.
Our simulations suggest that the G724S mutation selectively reduces osimertinib-binding affinity in the context of Ex19Del. Consistent with our simulations, cell lines transduced with Ex19Del/G724S demonstrate resistance to osimertinib, whereas cells transduced with L858R/G724S are sensitive to osimertinib. Subsequent clinical genomic profiling data further suggest G724S occurs with Ex19Del but not L858R. Furthermore, we demonstrate that Ex19Del/G724S retains sensitivity to afatinib, but not to erlotinib, suggesting a possible therapy for patients at the time of disease relapse.
Altogether, these data suggest that G724S is an allele-specific resistance mutation emerging in the context of Ex19Del but not L858R. Our results fundamentally reframe the problem of targeted therapy resistance from one focused on the "drug-resistance mutation" pair to one focused on the "activating mutation-drug-resistance mutation" trio. This has broad implications across clinical oncology.
Horizontal gene transfer (HGT) and gene duplication are often considered as separate mechanisms driving the evolution of new functions. However, the mobile genetic elements (MGEs) implicated in HGT ...can copy themselves, so positive selection on MGEs could drive gene duplications. Here, we use a combination of modeling and experimental evolution to examine this hypothesis and use long-read genome sequences of tens of thousands of bacterial isolates to examine its generality in nature. Modeling and experiments show that antibiotic selection can drive the evolution of duplicated antibiotic resistance genes (ARGs) through MGE transposition. A key implication is that duplicated ARGs should be enriched in environments associated with antibiotic use. To test this, we examined the distribution of duplicated ARGs in 18,938 complete bacterial genomes with ecological metadata. Duplicated ARGs are highly enriched in bacteria isolated from humans and livestock. Duplicated ARGs are further enriched in an independent set of 321 antibiotic-resistant clinical isolates. Our findings indicate that duplicated genes often encode functions undergoing positive selection and horizontal gene transfer in microbial communities.
The human motor system rapidly adapts to systematic perturbations but the adapted behavior seems to be forgotten equally rapidly. The reason for this forgetting is unclear, as is how to overcome it ...to promote long-term learning. Here we show that adapted behavior can be stabilized by a period of binary feedback about success and failure in the absence of vector error feedback. We examined the time course of decay after adaptation to a visuomotor rotation through a visual error-clamp condition--trials in which subjects received false visual feedback showing perfect directional performance, regardless of the movements they actually made. Exposure to this error-clamp following initial visuomotor adaptation led to a rapid reversion to baseline behavior. In contrast, exposure to binary feedback after initial adaptation turned the adapted state into a new baseline, to which subjects reverted after transient exposure to another visuomotor rotation. When both binary feedback and vector error were present, some subjects exhibited rapid decay to the original baseline, while others persisted in the new baseline. We propose that learning can be decomposed into two components--a fast-learning, fast-forgetting adaptation process that is sensitive to vector errors and insensitive to task success, and a second process driven by success that learns more slowly but is less susceptible to forgetting. These two learning systems may be recruited to different degrees across individuals. Understanding this competitive balance and exploiting the long-term retention properties of learning through reinforcement is likely to be essential for successful neuro-rehabilitation.
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