Transient changes in the firing of midbrain dopamine neurons have been closely tied to the unidimensional value-based prediction error contained in temporal difference reinforcement learning models. However, whereas an abundance of work has now shown how well dopamine responses conform to the predictions of this hypothesis, far fewer studies have challenged its implicit assumption that dopamine is not involved in learning value-neutral features of reward. Here, we review studies in rats and humans that put this assumption to the test, and which suggest that dopamine transients provide a much richer signal that incorporates information that goes beyond integrated value.

The major pathological feature of Parkinson 's disease (PD), the second most common neurodegenerative disease and most common movement disorder, is the predominant degeneration of dopaminergic neurons in the substantia nigra, a part of the midbrain. Despite decades of research, the molecular mechanisms of the origin of the disease remain unknown. While the disease was initially viewed as a purely neuronal disorder, results from single-cell transcriptomics have suggested that oligodendrocytes may play an important role in the early stages of Parkinson's. Although these findings are of high relevance, particularly to the search for effective disease-modifying therapies, the actual functional role of oligodendrocytes in Parkinson's disease remains highly speculative and requires a concerted scientific effort to be better understood. This Unsolved Mystery discusses the limited understanding of oligodendrocytes in PD, highlighting unresolved questions regarding functional changes in oligodendroglia, the role of myelin in nigral dopaminergic neurons, the impact of the toxic environment, and the aggregation of alpha-synuclein within oligodendrocytes.

Understanding how corticostriatal circuits mediate behavioral selection and initiation in a naturalistic setting is critical to understanding behavior choice and execution in unconstrained situations. The central striatum (CS) is well poised to play an important role in these spontaneous processes. Using fiber photometry and optogenetics, we identify a role for CS in grooming initiation. However, CS-evoked movements resemble short grooming fragments, suggesting additional input is required to appropriately sustain behavior once initiated. Consistent with this idea, the anterior lateral motor area (ALM) demonstrates a slow ramp in activity that peaks at grooming termination, supporting a potential role for ALM in encoding grooming bout length. Furthermore, optogenetic stimulation of ALM-CS terminals generates sustained grooming responses. Finally, dual-region photometry indicates that CS activation precedes ALM during grooming. Taken together, these data support a model in which CS is involved in grooming initiation, while ALM may encode grooming bout length.

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Recent advances in the field of Voltage Imaging, with a special focus on new constructs and novel implementations.

Work related to place tuning, spatial navigation, orientation and direction. Mainly includes articles on connectivity in the hippocampus, retrosplenial cortex, and related areas.

Objective. Clinical trials of the photovoltaic subretinal prosthesis PRIMA demonstrated feasibility of prosthetic central vision with resolution matching its 100 m pixel size. To improve prosthetic acuity further, pixel size should be decreased. However, there are multiple challenges, one of which is related to accommodating a compact shunt resistor within each pixel that discharges the electrodes between stimulation pulses and helps increase the contrast of the electric field pattern. Unfortunately, standard materials used in integrated circuit resistors do not match the resistivity required for small photovoltaic pixels. Therefore, we used a novel material - doped amorphous silicon (a-Si) and integrated it into photovoltaic arrays with pixel sizes down to 20 m. Approach. To fit within a few m2 area of the pixels and provide resistance in the M{Omega} range, the material should have sheet resistance of a few hundred k{Omega}/sq, which translates to resistivity of a few {Omega}*cm. The a-Si layer was deposited by low-pressure chemical vapor deposition (LPCVD) and its resistivity was adjusted by PH3 doping before encapsulating the resistors between SiO2 and SiC for stability in-vivo. Main Results. High-resolution retinal implants with integrated shunt resistors were fabricated with values ranging from 0.75 to 4 M{Omega} on top of the photovoltaic pixels of 55, 40, 30 and 20 m in size. Photoresponsivity with all pixel sizes was approximately 0.53 A/W, as high as in the arrays with no shunt resistor. The shunts shortened electrodes discharge time, with the average electric potential in electrolyte decreasing by only 21-31% when repetition rate increased from 2 to 30 Hz, as opposed to a 54-55% decrease without a shunt. Similarly, contrast of a Landolt C pattern increased from 16-22% with no shunt to 22-34% with a shunt. Further improvement in contrast is expected with pillar electrodes and local returns within each pixel. Significance. Miniature shunt resistors in a MOhm range can be fabricated from doped a-Si in a process compatible with manufacturing of photovoltaic arrays. The shunt resistors improved current injection and spatial contrast at video frame rates, without compromising the photoresponsivity. These advances are critical for scaling pixel sizes below 100 m to improve visual acuity of prosthetic vision.

Genome-scale metabolic models (GEMs) predict emergent phenotypes by modeling the metabolic networks encoded in genomes. While GEMs have significantly advanced systems biology, metabolic engineering, biomedicine, and environmental science, they require extensive time and resources for manual curation, which can limit their utility in rapidly evolving research landscapes. Recent findings suggest that manually curated reactions can sometimes reduce prediction accuracy, indicating that integrating additional biologically grounded constraints may better capture emergent phenotypes. One promising approach is the incorporation of enzyme allocation constraints, which has been shown to enhance the predictive accuracy in metabolic models. Enzymatically constrained GEMs (ecGEMs) rely on enzyme turnover rates (kcat) and protein molecular weights (MWs) to account for intracellular resource limitations by introducing an enzyme pool variable and assigning costs to reactions, thereby simulating enzymatic resource constraints. Tools such as GECKO, AutoPACMEN, and ECMpy provide computational pipelines for ecGEM generation. However, these pipelines are often limited by their reliance on experimentally measured kcat values or deep learning-predicted values, such as those generated by DLKcat, which face challenges in predicting kinetics for enzymes dissimilar to their training data. Additionally, these methods frequently require extensive manual curation of kcat values based on empirical data, a time-intensive process that hampers scalability and applicability to non-model organisms. To address these limitations, we introduce EMMAi (Enzyme-constrained Metabolic Models with AI), a pipeline that fully automates the incorporation of enzyme constraints into GEMs. Unlike existing pipelines, EMMAi exclusively utilizes kcat values predicted by UniKP, an AI framework with improved accuracy over DLKcat, particularly for enzymes not present in training datasets. UniKP achieves a 13% improvement in correlation for unseen enzymes, enabling EMMAi to deliver ecGEMs with enhanced prediction accuracy without manual curation requirements. We evaluated EMMAi by applying it to three GEMs: two manually curated models, iJO1366 (Escherichia coli str. K-12 substr. MG1655) and iMO1056 (Pseudomonas aeruginosa PAO1), and one draft GEM constructed and gap-filled using CarveMe. EMMAi-generated ecGEMs showed an average Pearson Correlation Coefficient (PCC) improvement of 0.27 for manually curated GEMs when compared to predicted and experimentally measured growth rates and Biolog readings. Notably, for the draft GEM of Pseudomonas aeruginosa PAO1, the PCC improved dramatically from -0.3 to 0.6. EMMAi demonstrates that automating the integration of enzyme allocation constraints using AI-predicted kinetic parameters significantly enhances the prediction accuracy of GEMs, even in the absence of manual curation. These results underscore EMMAi\'s potential as a scalable, efficient, and accurate tool for advancing GEM-based research in systems biology, metabolic engineering, and beyond.