Concert recording 2021-11-13

[Track 1]. Pastorale, BWV 590 / J.S. Bach -- [Track 2]. Suite no. 3 in C major for cello solo, BWV 1009. Prelude ; Allemande ; Courante ; Sarabande ; Boureé I & II ; Gigue / Bach -- [Track 3]. Pezzo capriccioso, op. 62 / Pyotr Tchaikovsky -- [Track 4]. When you wish upon a star -- [Track 5]. Mother goose suite. Pavane de la belle au bois dormant / Maurice Ravel -- Songs my mother taught me / Antonin Dvorák/Kreisler -- [Track 6]. Hansel and Gretel. Evening prayer / Engelbert Humperdinck -- [Track 7]. Cendrillon. Cinderella’s stepsisters / Jules Massenet -- [Track 8]. A frog he went a-courting / Paul Hindemith -- [Track 9]. Orfeo. Melodie / Christoph Gluck -- [Track 10]. Mother Goose suite. The fairy garden / Ravel

Large-Scale Covariability Between Aerosol and Precipitation Over the 7-SEAS Region: Observations and Simulations

One of the seven scientific areas of interests of the 7-SEAS field campaign is to evaluate the impact of aerosol on cloud and precipitation (http://7-seas.gsfc.nasa.gov). However, large-scale covariability between aerosol, cloud and precipitation is complicated not only by ambient environment and a variety of aerosol effects, but also by effects from rain washout and climate factors. This study characterizes large-scale aerosol-cloud-precipitation covariability through synergy of long-term multi ]sensor satellite observations with model simulations over the 7-SEAS region [10S-30N, 95E-130E]. Results show that climate factors such as ENSO significantly modulate aerosol and precipitation over the region simultaneously. After removal of climate factor effects, aerosol and precipitation are significantly anti-correlated over the southern part of the region, where high aerosols loading is associated with overall reduced total precipitation with intensified rain rates and decreased rain frequency, decreased tropospheric latent heating, suppressed cloud top height and increased outgoing longwave radiation, enhanced clear-sky shortwave TOA flux but reduced all-sky shortwave TOA flux in deep convective regimes; but such covariability becomes less notable over the northern counterpart of the region where low ]level stratus are found. Using CO as a proxy of biomass burning aerosols to minimize the washout effect, large-scale covariability between CO and precipitation was also investigated and similar large-scale covariability observed. Model simulations with NCAR CAM5 were found to show similar effects to observations in the spatio-temporal patterns. Results from both observations and simulations are valuable for improving our understanding of this region's meteorological system and the roles of aerosol within it. Key words: aerosol; precipitation; large-scale covariability; aerosol effects; washout; climate factors; 7- SEAS; CO; CAM

Expanding the diversity of mycobacteriophages: insights into genome architecture and evolution.

Mycobacteriophages are viruses that infect mycobacterial hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. All mycobacteriophages characterized to date are dsDNA tailed phages, and have either siphoviral or myoviral morphotypes. However, their genetic diversity is considerable, and although sixty-two genomes have been sequenced and comparatively analyzed, these likely represent only a small portion of the diversity of the mycobacteriophage population at large. Here we report the isolation, sequencing and comparative genomic analysis of 18 new mycobacteriophages isolated from geographically distinct locations within the United States. Although no clear correlation between location and genome type can be discerned, these genomes expand our knowledge of mycobacteriophage diversity and enhance our understanding of the roles of mobile elements in viral evolution. Expansion of the number of mycobacteriophages grouped within Cluster A provides insights into the basis of immune specificity in these temperate phages, and we also describe a novel example of apparent immunity theft. The isolation and genomic analysis of bacteriophages by freshman college students provides an example of an authentic research experience for novice scientists

Understanding implicit sensorimotor adaptation as a process of kinesthetic re-alignment

From elementary skills such as walking and talking, to complex ones such as playing tennis or music, humans are remarkably adept at learning to use their bodies in a coordinated manner. However, these abilities can be fragile: Many neurological conditions can compromise motor performance and learning. Understanding how the brain produces skilled movement will not only elucidate principles of learning but can also optimize rehabilitation interventions for individuals with movement disorders.Motor learning is not a unitary operation but relies on multiple learning processes (Kim, Avraham, and Ivry 2020; Krakauer et al. 2019). For example, reinforcement learning helps us select rewarding actions (Dayan and Daw 2008), use-dependent learning helps us rapidly execute well-practiced actions (Verstynen and Sabes 2011; Classen et al. 1998), and sensorimotor adaptation keeps our movements well-calibrated in response to changes in the body and environment (Helmholtz 1924; Stratton 1896). In addition, recent work has highlighted how these implicit processes may be complemented by explicit processes (Codol, Holland, and Galea 2018; Collins and Frank 2012; Marinovic et al. 2017; Jonathan S. Tsay, Kim, Saxena, et al. 2022). For example, when asked to move in a novel environment in which the visual feedback is altered (e.g., prism glasses), participants may adopt a re-aiming strategy to nullify the perturbation. Unlike implicit forms of learning, explicit processes allow for rapid changes in performance (Kim, Avraham, and Ivry 2020; Krakauer et al. 2019; Inoue et al. 2015; Smith, Ghazizadeh, and Shadmehr 2006; Schween et al. 2020; Daniel M. Wolpert and Flanagan 2016; Facchin et al. 2019). The joint operation of multiple learning processes has made it difficult to characterize features inherent to each process. To address this, new analytical methods have been recently developed to isolate individual components (Brudner et al. 2016; Jonathan S. Tsay, Haith, Ivry, et al. 2022; Marinovic et al. 2017; Yang, Cowan, and Haith 2021), providing new opportunities to revisit classic problems in sensorimotor learning: What is the critical signal driving learning for different processes? Are there limits to plasticity, and does this vary between processes? How does the quality of sensory feedback impact different components of motor learning? I exploit these methods in this dissertation to revisit the mechanisms at play in sensorimotor adaptation. Implicit adaptation has been framed as an iterative process designed to minimize sensory prediction error, the mismatch between a desired and experienced sensory outcome (Donchin, Francis, and Shadmehr 2003; R. Morehead and Smith 2017; Albert et al. 2022, 2021; Herzfeld et al. 2014; Kim et al. 2018; Thoroughman and Shadmehr 2000). Traditionally, the focus has been on how visual sensory prediction errors are used to modify a visuomotor map, ensuring that future movements are more accurate. According to this visuo-centric view, the upper bound of implicit adaptation represents a point of equilibrium, one at which the trial-by-trial change in hand position in response to a visual error is counterbalanced by a trial-by-trial decay (‘forgetting’) of this modified visuomotor map back to its baseline, default state. Despite its appeal, the visuo-centric view is an oversimplification. The brain exploits information from all of our senses, not only from vision (Ernst and Banks 2002; Van Beers, Sittig, and Gon 1999; Chancel, Ehrsson, and Ma 2022; Sober and Sabes 2005, 2003). This insight, paired with the empirical data outlined in this dissertation, have inspired a new, ‘kinesthetic re-alignment’ model of implicit adaptation (Jonathan S. Tsay, Kim, Haith, et al. 2022). By this view, implicit adaptation is an iterative process designed to minimize a ‘kinesthetic’ sensory prediction error, the misalignment between the perceived heading angle and the movement goal. The perceived hand position is a composite signal, reflecting the seen hand position (via visual afferents), the felt hand position (via peripheral proprioceptive afferents based on mechanoreceptors from muscles, joints, and skin), the predicted hand position (via the efferent motor command), and the movement goal (via a prior belief that the movement will be successful). Implicit adaptation will cease when the kinesthetic error is nullified, that is, when the perceived hand position and the movement goal are re-aligned. (Footnote: Whereas we had used ‘proprioception' in our published work featured in this dissertation, we will adopt the term “kinesthesia” here in the Abstract given that the perceived hand is a composite kinesthetic representation that encompasses both central beliefs and peripheral senses (Proske and Gandevia 2012)).In Chapter 1, I tested a core assumption held by studies of implicit sensorimotor adaptation, namely that the perceived hand position is at the target (subject to random noise). Specifically, we used a novel visuomotor task that isolated implicit adaptation and probed kinesthesia in a fine-grain manner (i.e., the participant’s perceived heading position on each trial). Whereas participants exhibited robust implicit adaptation (i.e., changes in hand position away from the target in the opposite direction of the visual error), their perceived hand position remained near the target. However, to our surprise, the position reports exhibited a non-monotonic function over the course of adaptation: The participants initially perceived their hand to be biased towards the perturbed visual feedback, mis-aligned with the movement goal. However, over time the reports shifted away from the perturbed visual feedback, re-aligning back to the target. Together, these data not only revealed unappreciated kinesthetic changes that arise during learning but also seeded the idea for a kinesthetic re-alignment perspective of implicit adaptation. In Chapter 2, I evaluate whether there is the relationship between kinesthetic perception and implicit adaptation, one that would not be predicted by visuocentric models. By using two visuomotor tasks that isolated implicit adaptation and probed kinesthesia, we discovered that participants who have greater kinesthetic biases towards the perturbed visual feedback and greater baseline kinesthetic uncertainty exhibited greater implicit adaptation. As such, these data provided evidence for new, unexplained kinesthetic constraints on the extent of implicit adaptation, supporting the notion that kinesthetic perception plays a critical role in implicit adaptation. The empirical results from the previous chapters led us to develop a new, kinesthetic re-alignment model of implicit adaptation. I will formalize this model in Chapter 3, demonstrating how it readily explains the non-monotonic time course of perceived hand position during implicit adaptation (Chapter 1 and the relationship between kinesthetic biases/uncertainty with the extent of implicit adaptation (Chapter 2). Moreover, I will demonstrate how the kinesthetic re-alignment model is also able to capture a myriad of observations not accounted for by a visuo-centric view of adaptation. Taken together, the kinesthetic re-alignment model brings us one step closer to a more holistic view of motor adaptation, a perspective that formalizes how our high-level beliefs and low-level senses inform where we are positioned and how we are to adapt

Perturbation Variability Does Not Influence Implicit Sensorimotor Adaptation.

Implicit adaptation has been regarded as a rigid process that automatically operates in response to movement errors to keep the sensorimotor system precisely calibrated. This hypothesis has been challenged by recent evidence suggesting flexibility in this learning process. One compelling line of evidence comes from work suggesting that this form of learning is context-dependent, with the rate of learning modulated by error history. Specifically, learning was attenuated in the presence of perturbations exhibiting high variance compared to when the perturbation is fixed. However, these findings are confounded by the fact that the adaptation system corrects for errors of different magnitudes in a non-linear manner, with the adaptive response increasing in a proportional manner to small errors and saturating to large errors. Through simulations, we show that this non-linear motor correction function is sufficient to explain the effect of perturbation variance without referring to an experience-dependent change in error sensitivity. Moreover, by controlling the distribution of errors experienced during training, we provide empirical evidence showing that there is no measurable effect of perturbation variance on implicit adaptation. As such, we argue that the evidence to date remains consistent with the rigidity assumption