Fluorescence microscopy is essential for biological research, offering high-contrast imaging of microscopic structures. However, the quality of these images is often compromised by optical aberrations and noise, particularly in low signal-to-noise ratio (SNR) conditions. While adaptive optics (AO) can correct aberrations, it requires costly hardware and slows down imaging; whereas current denoising approaches boost the SNR but leave out the aberration compensation. To address these limitations, we introduce HD2Net, a deep learning framework that enhances image quality by simultaneously denoising and suppressing the effect of aberrations without the need for additional hardware. Building on our previous work, HD2Net incorporates noise estimation and aberration removal modules, effectively restoring images degraded by noise and aberrations. Through comprehensive evaluation of synthetic phantoms and biological data, we demonstrate that HD2Net outperforms existing methods, significantly improving image resolution and contrast. This framework offers a promising solution for enhancing biological imaging, particularly in challenging aberrating and low-light conditions.

Fluorescence microscopy is essential for biological research, offering high-contrast imaging of microscopic structures. However, the quality of these images is often compromised by optical aberrations and noise, particularly in low signal-to-noise ratio (SNR) conditions. While adaptive optics (AO) can correct aberrations, it requires costly hardware and slows down imaging; whereas current denoising approaches boost the SNR but leave out the aberration compensation. To address these limitations, we introduce HD2Net, a deep learning framework that enhances image quality by simultaneously denoising and suppressing the effect of aberrations without the need for additional hardware. Building on our previous work, HD2Net incorporates noise estimation and aberration removal modules, effectively restoring images degraded by noise and aberrations. Through comprehensive evaluation of synthetic phantoms and biological data, we demonstrate that HD2Net outperforms existing methods, significantly improving image resolution and contrast. This framework offers a promising solution for enhancing biological imaging, particularly in challenging aberrating and low-light conditions.

Intracellular recordings are routinely used to study the synaptic and intrinsic properties of neurons in vitro. A key requirement for these recordings is a mechanically very stable preparation; thus their use in vivo had been limited previously to head-restrained animals. We have recently demonstrated that anchoring the electrode rigidly in place with respect to the skull provides sufficient stabilization for long-lasting, high-quality whole-cell recordings in awake, freely moving rats. This protocol describes our procedure in detail, adds specific instructions for targeting hippocampal CA1 pyramidal neurons and updates it with changes that facilitate patching and improve the success rate. The changes involve combining a standard, nonhead-mounted micromanipulator with a gripper to firmly hold the recording pipette during the anchoring process then gently release it afterwards. The procedure from the beginning of surgery to the end of a recording takes approximately 5 h. This technique allows new studies of the mechanisms underlying neuronal integration and cellular/synaptic plasticity in identified cells during natural behaviors.

Hedonic eating is defined as food consumption driven by palatability without physiological need. However, neural control of palatable food intake is poorly understood. We discovered that hedonic eating is controlled by a neural pathway from the peri-locus ceruleus to the ventral tegmental area (VTA). Using photometry-calibrated optogenetics, we found that VTA dopamine (VTA) neurons encode palatability to bidirectionally regulate hedonic food consumption. VTA neuron responsiveness was suppressed during food consumption by semaglutide, a glucagon-like peptide receptor 1 (GLP-1R) agonist used as an antiobesity drug. Mice recovered palatable food appetite and VTA neuron activity during repeated semaglutide treatment, which was reversed by consumption-triggered VTA neuron inhibition. Thus, hedonic food intake activates VTA neurons, which sustain further consumption, a mechanism that opposes appetite reduction by semaglutide.

PURPOSE: The aim of the study was to examine acculturation and established risk factors in explaining variation in periodontitis prevalence among Hispanic/Latino subgroups.

METHODS: Participants were 12,730 dentate adults aged 18-74 years recruited into the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) from four U.S. field centers between 2008 and 2011. A standardized periodontal assessment measured probing pocket depth and gingival recession at six sites per tooth for up to 28 teeth. Periodontitis was defined according to the Centers for Disease Control and Prevention and American Academy of Periodontology case classifications developed for population surveillance. Covariates included acculturation indicators and established periodontitis risk factors. Survey estimation procedures took account of the complex sampling design. Adjusted multivariate binomial regression estimated prevalence ratios and 95% confidence limits (CLs).

RESULTS: Unadjusted prevalence of moderate and severe periodontitis was 38.5% and ranged from 24.7% among Dominicans to 52.1% among Cubans. Adjusted prevalence ratios for subgroups relative to Dominicans were as follows: (1) 1.34 (95% CL, 1.13-1.58) among South Americans; (2) 1.37 (95% CL, 1.17-1.61) among Puerto Ricans; (3) 1.43 (95% CL, 1.25-1.64) among Mexicans; (4) 1.53 (95% CL, 1.32-1.76) among Cubans; and (5) 1.55 (95% CL, 1.35-1.78) among Central Americans.

CONCLUSIONS: Heterogeneity in prevalence of moderate/severe periodontitis among Hispanic/Latino subpopulations was not explained by acculturation or periodontitis risk factors.

The mechanistic operation of brain regions is often interpreted by partitioning constituent neurons into 'cell types'. Historically, such cell types were broadly defined by their correspondence to gross features of the nervous system (such as cytoarchitecture). Modern-day neuroscientific techniques, enabling a more nuanced examination of neuronal properties, have illustrated a wealth of heterogeneity within these classical cell types. Here, we review the extent of this within-cell-type heterogeneity in one of the simplest cortical regions of the mammalian brain, the rodent hippocampus. We focus on the mounting evidence that the classical CA3, CA1 and subiculum pyramidal cell types all exhibit prominent and spatially patterned within-cell-type heterogeneity, and suggest these cell types provide a model system for exploring the organization and function of such heterogeneity. Given that the hippocampus is structurally simple and evolutionarily ancient, within-cell-type heterogeneity is likely to be a general and crucial feature of the mammalian brain.

Although associative learning has been localized to specific brain areas in many animals, identifying the underlying synaptic processes in vivo has been difficult. Here, we provide the first demonstration of long-term synaptic plasticity at the output site of the Drosophila mushroom body. Pairing an odor with activation of specific dopamine neurons induces both learning and odor-specific synaptic depression. The plasticity induction strictly depends on the temporal order of the two stimuli, replicating the logical requirement for associative learning. Furthermore, we reveal that dopamine action is confined to and distinct across different anatomical compartments of the mushroom body lobes. Finally, we find that overlap between sparse representations of different odors defines both stimulus specificity of the plasticity and generalizability of associative memories across odors. Thus, the plasticity we find here not only manifests important features of associative learning but also provides general insights into how a sparse sensory code is read out.

Gap junctions play an important role in the regulation of neuronal metabolism and homeostasis by serving as connections that enable small molecules to pass between cells and synchronize activity between cells. Although recent studies have linked gap junctions to memory formation, it remains unclear how they contribute to this process. Gap junctions are hexameric hemichannels formed from the connexin and pannexin gene families in chordates and the innexin (inx) gene family in invertebrates. Here we show that two modulatory neurons, the anterior paired lateral (APL) neuron and the dorsal paired medial (DPM) neuron, form heterotypic gap junctions within the mushroom body (MB), a learning and memory center in the Drosophila brain. Using RNA interference-mediated knockdowns of inx7 and inx6 in the APL and DPM neurons, respectively, we found that flies showed normal olfactory associative learning and intact anesthesia-resistant memory (ARM) but failed to form anesthesia-sensitive memory (ASM). Our results reveal that the heterotypic gap junctions between the APL and DPM neurons are an essential part of the MB circuitry for memory formation, potentially constituting a recurrent neural network to stabilize ASM.

Animals use rules to initiate behaviors. Such rules are often described as triggers that determine when behavior begins. However, although less explored, these selection rules are also an opportunity to establish sensorimotor constraints that influence how the behavior ends. These constraints may be particularly significant in influencing success in prey capture. Here we explore this in dragonfly prey interception. We found that in the moments leading up to takeoff, perched dragonflies employ a series of sensorimotor rules that determine the time of takeoff and increase the probability of successful capture. First, the dragonfly makes a head saccade followed by smooth pursuit movements to orient its direction-of-gaze at potential prey. Second, the dragonfly assesses whether the prey's angular size and speed co-vary within a privileged range. Finally, the dragonfly times the moment of its takeoff to a prediction of when the prey will cross the zenith. Each of these processes serves a purpose. The angular size-speed criteria biases interception flights to catchable prey, while the head movements and the predictive takeoff ensure flights begin with the prey visually fixated and directly overhead-the key parameters that underlie interception steering. Prey that do not elicit takeoff generally fail at least one of the criterion, and the loss of prey fixation or overhead positioning during flight is strongly correlated with terminated flights. Thus from an abundance of potential targets, the dragonfly selects a stereotyped set of takeoff conditions based on the prey and body states most likely to end in successful capture.

Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases.