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“Lesson study” has garnered considerable attention from educational researchers and practitioners as a promising method for improving instruction. At its core, lesson study reflects a collaborative inquiry process, grounded in cycles of planning, enacting, and reflecting, that promotes teacher learning through active engagement in lesson refinement. In this article, we leverage lesson study as an exemplar case to foreground an examination and application of theory-based evaluation. We overview a novel evaluation framework contextualized to this multidimensional instructional program and draw out the contributions of theory-based evaluation to the study of program effects. To ground the description of this framework, we summarize representative findings from our ongoing evaluation of a lesson study-based instructional program and discuss processes for selecting, using, and managing evidence sources as well as determining causal strands supporting program outcomes. We conclude by discussing lessons learned and implications for theory-based evaluation of complex programs. 

Some of the most important tasks of visual and motor systems involve estimating the motion of objects and tracking them over time. Such systems evolved to meet the behavioral needs of the organism in its natural environment, and may therefore be adapted to the statistics of motion it is likely to encounter. By tracking the movement of individual points in movies of natural scenes, we begin to identify common properties of natural motion across scenes. As expected, objects in natural scenes move in a persistent fashion, with velocity correlations lasting hundreds of milliseconds. More subtly, but crucially, we find that the observed velocity distributions are heavy-tailed and can be modeled as a Gaussian scale-mixture. Extending this model to the time domain leads to a dynamic scale-mixture model, consisting of a Gaussian process multiplied by a positive scalar quantity with its own independent dynamics. Dynamic scaling of velocity arises naturally as a consequence of changes in object distance from the observer, and may approximate the effects of changes in other parameters governing the motion in a given scene. This modeling and estimation framework has implications for the neurobiology of sensory and motor systems, which need to cope with these fluctuations in scale in order to represent motion efficiently and drive fast and accurate tracking behavior. 

Abstract The capacity of aquatic systems to buffer acidification depends on the sum contributions of various chemical species to total alkalinity (TA). Major TA contributors are inorganic, with carbonate and bicarbonate considered the most important. However, growing evidence shows that many rivers, estuaries, and coastal waters contain dissolved organic molecules with charge sites that create organic alkalinity (OrgAlk). This study describes the first comparison of (1) OrgAlk distributions and (2) acid–base properties in contrasting estuary‐plume systems: the Pleasant (Maine, USA) and the St. John (New Brunswick, CA). The substantial concentrations of OrgAlk in each estuary were sometimes not conservative with salinity and typically associated with very low pH. Two approaches to OrgAlk measurement showed consistent differences, indicating acid–base characteristics inconsistent with the TA definition. The OrgAlk fraction of TA ranged from 78% at low salinity to less than 0.4% in the coastal ocean endmember. Modeling of titration data identified three groups of organic charge sites, with mean acid–base dissociation constants (pK_a) of 4.2 (± 0.5), 5.9 (± 0.7) and 8.5 (± 0.2). These represented 21% (± 9%), 8% (± 5%), and 71% (± 11%) of titrated organic charge groups. Including OrgAlk, pK_a, and titrated organic charge groups in carbonate system calculations improved estimates of pH. However, low and medium salinity, organic‐rich samples demonstrated persistent offsets in calculated pH, even using dissolved inorganic carbon and CO₂partial pressure as inputs. These offsets show the ongoing challenge of carbonate system intercomparisons in organic rich systems whereby new techniques and further investigations are needed to fully account for OrgAlk in TA titrations. 

Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells, less is known about how populations form both flexible and reliable encoding in natural moving scenes. We record from the larval salamander retina responding to five different natural movies, over many repeats, and use these data to characterize the population code in terms of single-cell fluctuations in rate and pairwise couplings between cells. Decomposing the population code into independent and cell-cell interactions reveals how broad scene structure is encoded in the retinal output. while the single-cell activity adapts to different stimuli, the population structure captured in the sparse, strong couplings is consistent across natural movies as well as synthetic stimuli. We show that these interactions contribute to encoding scene identity. We also demonstrate that this structure likely arises in part from shared bipolar cell input as well as from gap junctions between retinal ganglion cells and amacrine cells. 

We present an interview study of 6th grade math and science teachers’ expressed goals for engaging their students with data. We explored this across disciplinary boundaries to contribute to a body of knowledge that can support the development of a more coherent experience for students across math and science classes. Our teachers were all highly motivated to engage their students with data, and all wanted their students to see things with their data models. However, we observed consequential differences in the kinds of things they wanted students to see. Here we describe these differences and discuss potential implications for practice. 

Mammalian axonal development begins in embryonic stages and continues postnatally. After birth, axonal proteomic landscape changes rapidly, coordinated by transcription, protein turnover, and post-translational modifications. Comprehensive profiling of axonal proteomes across neurodevelopment is limited, with most studies lacking cell-type and neural circuit specificity, resulting in substantial information loss. We create a Cre-dependent APEX2 reporter mouse line and map cell-type-specific proteome of corticostriatal projections across postnatal development. We synthesize analysis frameworks to define temporal patterns of axonal proteome and phosphoproteome, identifying co-regulated proteins and phosphorylations associated with genetic risk for human brain disorders. We discover proline-directed kinases as major developmental regulators. APEX2 transgenic reporter proximity labeling offers flexible strategies for subcellular proteomics with cell type specificity in early neurodevelopment, a critical period for neuropsychiatric disease. 

Abstract Estuaries may be uniquely susceptible to the combined acidification pressures of atmospherically driven ocean acidification (OA), biologically driven CO 2 inputs from the estuary itself, and terrestrially derived freshwater inputs. This study utilized continuous measurements of total alkalinity (TA) and the partial pressure of carbon dioxide (pCO 2 ) from the mouth of Great Bay, a temperate northeastern U.S. estuary, to examine the potential influences of endmember mixing and biogeochemical transformation upon estuary buffering capacity ( β – H ). Observations were collected hourly over 28 months representing all seasons between May 2016 and December 2019. Results indicated that endmember mixing explained most of the observed variability in TA and dissolved inorganic carbon (DIC), concentrations of which varied strongly with season. For much of the year, mixing dictated the relative proportions of salinity‐normalized TA and DIC as well, but a fall season shift in these proportions indicated that aerobic respiration was observed, which would decrease β – H by decreasing TA and increasing DIC. However, fall was also the season of weakest statistical correspondence between salinity and both TA and DIC, as well as the overall highest salinity, TA and β – H . Potential biogeochemically driven β – H decreases were overshadowed by increased buffering capacity supplied by coastal ocean water. A simple modeling exercise showed that mixing processes controlled most monthly changes in TA and DIC, obscuring impacts from air–sea exchange or metabolic processes. Advective mixing contributions may be as important as biogeochemically driven changes to observe when evaluating local estuarine and coastal OA.