A fundamental goal in neuroscience is to uncover common principles by which different modalities of information are processed. In the mammalian brain, thalamus acts as the essential hub for forebrain circuits handling inputs from sensory, motor, limbic, and cognitive pathways. Whether thalamus imposes common transformations on each of these modalities is unknown. Molecular characterization offers a principled approach to revealing the organization of thalamus. Using near-comprehensive and projection-specific transcriptomic sequencing, we found that almost all thalamic nuclei fit into one of three profiles. These profiles lie on a single axis of genetic variance which is aligned with the mediolateral spatial axis of thalamus. Genes defining this axis of variance include receptors and ion channels, providing a systematic diversification of input/output transformations across the topography of thalamus. Single cell transcriptional profiling revealed graded heterogeneity within individual thalamic nuclei, demonstrating that a spectrum of cell types and potentially diverse input/output transforms exist within a given thalamic nucleus. Together, our data argue for an archetypal organization of pathways serving diverse input modalities, and provides a comprehensive organizational scheme for thalamus.

Amyloid-β (Aβ) and human islet amyloid polypeptide (hIAPP) aggregate to form amyloid fibrils that deposit in tissues, and are associated with Alzheimer's disease (AD) and Type-II Diabetes (T2D), respectively. Individuals with T2D have an increased risk of developing AD, and conversely, AD patients have an increased risk of developing T2D. Evidence suggests that this link between AD and T2D might originate from a structural similarity between aggregates of Aβ and hIAPP. Using the cryoEM method Micro-Electron Diffraction (MicroED) we determined the atomic structures of 11-residue segments from both Aβ and hIAPP, termed Aβ 24-34 WT and hIAPP 19-29 S20G, with 64% sequence similarity. We observe a high degree of structural similarity between their backbone atoms (0.96 Å RMSD). Moreover, fibrils of these segments induce amyloid formation through self- and cross-seeding. Furthermore, inhibitors designed for one segment show cross-efficacy for full-length Aβ and hIAPP and reduce cytotoxicity of both proteins, though by apparently blocking different cytotoxic mechanisms. The similarity of the atomic structures of Aβ 24-34 WT and hIAPP 19-29 S20G offers a molecular model for cross-seeding between Aβ and hIAPP.

Coherent control of purposive actions emerges from the coordination of multiple brain circuits during learning. Dissociable brain circuits and cell-types are thought to preferentially participate in distinct learning mechanisms. For example, the activity of midbrain dopamine (mDA) neurons is proposed to primarily, or even exclusively, reflect reward prediction error signals in well-trained animals. To study the specific contribution of individual circuits requires observing changes before tight functional coordination is achieved. However, little is known about the detailed timing of the emergence of reward-related representations in dopaminergic neurons. Here we recorded activity of identified dopaminergic neurons as naive mice learned a novel stimulus-reward association. We found that at early stages of learning mDA neuron activity reflected both external (sensory) and internal (action initiation) causes of reward expectation. The increasingly precise correlation of action initiation with sensory stimuli rather than an evaluation of outcomes governed mDA neuron activity. Thus, our data demonstrate that mDA neuron activity early in learning does not reflect errors, but is more akin to a Hebbian learning signal - providing new insight into a critical computation in a highly conserved, essential learning circuit.

Emerging applications that exploit the properties of nanoparticles for biotechnology require that the nanoparticles be biocompatible or support biological recognition. These types of particles can be produced through syntheses that involve biologically relevant molecules (proteins or natural extracts, for example). Many of the protocols that rely on these molecules are performed without a clear understanding of the mechanism by which the materials are produced. We have investigated a previously described reaction in which gold nanoparticles are produced from the reaction of chloroauric acid and proteins in solution. We find that modifications to the starting conditions can alter the product from the expected solution-suspended colloids to a product where colloids are formed within a solid, fibrous protein structure. We have interrogated this synthesis, exploiting the change in products to better understand this reaction. We have evaluated the kinetics and products for 7 different proteins over a range of concentrations and temperatures. The key factor that controls the synthetic outcome (colloid or fiber) is the concentration of the protein relative to the gold concentration. We find that the observed fibrous structures are more likely to form at low protein concentrations and when hydrophilic proteins are used. An analysis of the reaction kinetics shows that AuNP formation occurs faster at lower protein (fiber-forming) concentrations than at higher protein (colloid-forming) concentrations. These results contradict traditional expectations for reaction kinetics and protein-fiber formation and are instructive of the manner in which proteins template gold nanoparticle production.

Animals adaptively respond to a tactile stimulus by choosing an ethologically relevant behavior depending on the location of the stimuli. Here, we investigate how somatosensory inputs on different body segments are linked to distinct motor outputs in Drosophila larvae. Larvae escape by backward locomotion when touched on the head, while they crawl forward when touched on the tail. We identify a class of segmentally repeated second-order somatosensory interneurons, that we named Wave, whose activation in anterior and posterior segments elicit backward and forward locomotion, respectively. Anterior and posterior Wave neurons extend their dendrites in opposite directions to receive somatosensory inputs from the head and tail, respectively. Downstream of anterior Wave neurons, we identify premotor circuits including the neuron A03a5, which together with Wave, is necessary for the backward locomotion touch response. Thus, Wave neurons match their receptive field to appropriate motor programs by participating in different circuits in different segments.

The behavioral state of an animal can dynamically modulate visual processing. In flies, the behavioral state is known to alter the temporal tuning of neurons that carry visual motion information into the central brain. However, where this modulation occurs and how it tunes the properties of this neural circuit are not well understood. Here, we show that the behavioral state alters the baseline activity levels and the temporal tuning of the first directionally selective neuron in the ON motion pathway (T4) as well as its primary input neurons (Mi1, Tm3, Mi4, Mi9). These effects are especially prominent in the inhibitory neuron Mi4, and we show that central octopaminergic neurons provide input to Mi4 and increase its excitability. We further show that octopamine neurons are required for sustained behavioral responses to fast-moving, but not slow-moving, visual stimuli in walking flies. These results indicate that behavioral-state modulation acts directly on the inputs to the directionally selective neurons and supports efficient neural coding of motion stimuli.

While building information modeling (BIM) is widely embraced by the architectural, engineering and construction (AEC) industry, BIM adoption in facilities management (FM) is still relatively new and limited. BIM deliverables from design and construction generally do not fulfill FM needs unless they are clearly specified and carefully managed.

The Facilities Group responsible for the Janelia Research Campus of the Howard Hughes Medical Institute (HHMI) expects any BIM platform to provide value in operations and maintenance. Janelia’s BIM vision goes beyond transferring BIM data to computerized maintenance management software (CMMS) and integrated workplace management system (IWMS) platforms. Instead, Janelia creates and maintains FM-capable BIM, utilizes the models to solve operational challenges and improves safety and efficiency in various ways, including engineering analysis for heating, ventilation and air conditioning (HVAC), electrical and plumbing; building automation systems (BAS) analysis; operational impact analysis; and BIM-aided operation safety.

Chemogenetic technologies enable selective pharmacological control of specific cell populations. An increasing number of approaches have been developed that modulate different signaling pathways. Selective pharmacological control over G protein-coupled receptor signaling, ion channel conductances, protein association, protein stability, and small molecule targeting allows modulation of cellular processes in distinct cell types. Here, we review these chemogenetic technologies and instances of their applications in complex tissues in vivo and ex vivo.

Advances in single-cell RNA-sequencing technology have resulted in a wealth of studies aiming to identify transcriptomic cell types in various biological systems. There are multiple experimental approaches to isolate and profile single cells, which provide different levels of cellular and tissue coverage. In addition, multiple computational strategies have been proposed to identify putative cell types from single-cell data. From a data generation perspective, recent single-cell studies can be classified into two groups: those that distribute reads shallowly over large numbers of cells and those that distribute reads more deeply over a smaller cell population. Although there are advantages to both approaches in terms of cellular and tissue coverage, it is unclear whether different computational cell type identification methods are better suited to one or the other experimental paradigm. This study reviews three cell type clustering algorithms, each representing one of three broad approaches, and finds that PCA-based algorithms appear most suited to low read depth data sets, whereas gene clustering-based and biclustering algorithms perform better on high read depth data sets. In addition, highly related cell classes are better distinguished by higher-depth data, given the same total number of reads; however, simultaneous discovery of distinct and similar types is better served by lower-depth, higher cell number data. Overall, this study suggests that the depth of profiling should be determined by initial assumptions about the diversity of cells in the population, and that the selection of clustering algorithm(s) is subsequently based on the depth of profiling will allow for better identification of putative transcriptomic cell types.