Interactive effects of participant and stimulus race on cognitive performance in youth: Insights from the ABCD study.

An extensive literature shows that race information can impact cognitive performance. Two key findings include an attentional bias to Black racial cues in U.S. samples and diminished recognition of other-race faces compared to same-race faces in predominantly White adult samples. Yet face stimuli are increasingly used in psychological research often unrelated to race (Conley et al., 2018) or without consideration for how race information may influence cognitive performance, especially among developmental participants from different racial groups. In the current study we used open-access data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study® 4.0.1 release to test for developmentally similar other- and same-race effects of Black and White face stimuli on attention, working memory, and recognition memory in 9- and 10-year-old Black and White children (n=5,659) living in the U.S. Black and White children showed better performance when attending to Black versus White faces. We also show an advantage in recognition memory of same-race compared to other-race faces in White children that did not generalize to Black children. Together the findings highlight how race information, even when irrelevant to an experiment, may indirectly lead to misinterpretation of group differences in cognitive performance in children of different racial backgrounds.

COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework.

The fast and accurate conformation space modeling is an essential part of computational approaches for solving ligand and structure-based drug discovery problems. Recent state-of-the-art diffusion models for molecular conformation generation show promising distribution coverage and physical plausibility metrics but suffer from a slow sampling procedure. We propose a novel adversarial generative framework, COSMIC, that shows comparable generative performance but provides a time-efficient sampling and training procedure. Given a molecular graph and random noise, the generator produces a conformation in two stages. First, it constructs a conformation in a rotation and translation invariant representation─internal coordinates. In the second step, the model predicts the distances between neighboring atoms and performs a few fast optimization steps to refine the initial conformation. The proposed model considers conformation energy, achieving comparable space coverage, and diversity metrics results.

Quantum computing for near-term applications in generative chemistry and drug discovery.

In recent years, drug discovery and life sciences have been revolutionized with machine learning and artificial intelligence (AI) methods. Quantum computing is touted to be the next most significant leap in technology; one of the main early practical applications for quantum computing solutions is predicted to be in quantum chemistry simulations. Here, we review the near-term applications of quantum computing and their advantages for generative chemistry and highlight the challenges that can be addressed with noisy intermediate-scale quantum (NISQ) devices. We also discuss the possible integration of generative systems running on quantum computers into established generative AI platforms.

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry.

De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly developing fields with lead candidate discovery in the drug development process. First, artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This article explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.

Cognitive influences on fixational eye movements.

We perceive the world based on visual information acquired via oculomotor control,1 an activity intertwined with ongoing cognitive processes.2,3,4 Cognitive influences have been primarily studied in the context of macroscopic movements, like saccades and smooth pursuits. However, our eyes are never still, even during periods of fixation. One of the fixational eye movements, ocular drifts, shifts the stimulus over hundreds of receptors on the retina, a motion that has been argued to enhance the processing of spatial detail by translating spatial into temporal information.5 Despite their apparent randomness, ocular drifts are under neural control.6,7,8 However little is known about the control of drift beyond the brainstem circuitry of the vestibulo-ocular reflex.9,10 Here, we investigated the cognitive control of ocular drifts with a letter discrimination task. The experiment was designed to reveal open-loop effects, i.e., cognitive oculomotor control driven by specific prior knowledge of the task, independent of incoming sensory information. Open-loop influences were isolated by randomly presenting pure noise fields (no letters) while subjects engaged in discriminating specific letter pairs. Our results show open-loop control of drift direction in human observers.

Engineering an autonomous VH domain to modulate intracellular pathways and to interrogate the eIF4F complex.

An attractive approach to target intracellular macromolecular interfaces and to model putative drug interactions is to design small high-affinity proteins. Variable domains of the immunoglobulin heavy chain (VH domains) are ideal miniproteins, but their development has been restricted by poor intracellular stability and expression. Here we show that an autonomous and disufhide-free VH domain is suitable for intracellular studies and use it to construct a high-diversity phage display library. Using this library and affinity maturation techniques we identify VH domains with picomolar affinity against eIF4E, a protein commonly hyper-activated in cancer. We demonstrate that these molecules interact with eIF4E at the eIF4G binding site via a distinct structural pose. Intracellular overexpression of these miniproteins reduce cellular proliferation and expression of malignancy-related proteins in cancer cell lines. The linkage of high-diversity in vitro libraries with an intracellularly expressible miniprotein scaffold will facilitate the discovery of VH domains suitable for intracellular applications.

Development of a novel peptide aptamer that interacts with the eIF4E capped-mRNA binding site using peptide epitope linker evolution (PELE).

Identifying new binding sites and poses that modify biological function are an important step towards drug discovery. We have identified a novel disulphide constrained peptide that interacts with the cap-binding site of eIF4E, an attractive therapeutic target that is commonly overexpressed in many cancers and plays a significant role in initiating a cancer specific protein synthesis program though binding the 5'cap (7'methyl-guanoisine) moiety found on mammalian mRNAs. The use of disulphide constrained peptides to explore intracellular biological targets is limited by their lack of cell permeability and the instability of the disulphide bond in the reducing environment of the cell, loss of which results in abrogation of binding. To overcome these challenges, the cap-binding site interaction motif was placed in a hypervariable loop on an VH domain, and then selections performed to select a molecule that could recapitulate the interaction of the peptide with the target of interest in a process termed Peptide Epitope Linker Evolution (PELE). A novel VH domain was identified that interacted with the eIF4E cap binding site with a nanomolar affinity and that could be intracellularly expressed in mammalian cells. Additionally, it was demonstrated to specifically modulate eIF4E function by decreasing cap-dependent translation and cyclin D1 expression, common effects of eIF4F complex disruption.

The Advent of Generative Chemistry.

Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.

Soma-Targeted Imaging of Neural Circuits by Ribosome Tethering.

Neuroscience relies on techniques for imaging the structure and dynamics of neural circuits, but the cell bodies of individual neurons are often obscured by overlapping fluorescence from axons and dendrites in surrounding neuropil. Here, we describe two strategies for using the ribosome to restrict the expression of fluorescent proteins to the neuronal soma. We show first that a ribosome-tethered nanobody can be used to trap GFP in the cell body, thereby enabling direct visualization of previously undetectable GFP fluorescence. We then design a ribosome-tethered GCaMP for imaging calcium dynamics. We show that this reporter faithfully tracks somatic calcium dynamics in the mouse brain while eliminating cross-talk between neurons caused by contaminating neuropil. In worms, this reporter enables whole-brain imaging with faster kinetics and brighter fluorescence than commonly used nuclear GCaMPs. These two approaches provide a general way to enhance the specificity of imaging in neurobiology.

Sustained NPY signaling enables AgRP neurons to drive feeding.

Artificial stimulation of Agouti-Related Peptide (AgRP) neurons promotes intense food consumption, yet paradoxically during natural behavior these cells are inhibited before feeding begins. Previously, to reconcile these observations, we showed that brief stimulation of AgRP neurons can generate hunger that persists for tens of minutes, but the mechanisms underlying this sustained hunger drive remain unknown (Chen et al., 2016). Here we show that Neuropeptide Y (NPY) is uniquely required for the long-lasting effects of AgRP neurons on feeding behavior. We blocked the ability of AgRP neurons to signal through AgRP, NPY, or GABA, and then stimulated these cells using a paradigm that mimics their natural regulation. Deletion of NPY, but not AgRP or GABA, abolished optically-stimulated feeding, and this was rescued by NPY re-expression selectively in AgRP neurons. These findings reveal a unique role for NPY in sustaining hunger in the interval between food discovery and consumption.

The Clarus Video System (Trachway) and direct laryngoscope for endotracheal intubation with cricoid pressure in simulated rapid sequence induction intubation: a prospective randomized controlled trial.

BACKGROUND: During an emergency endotracheal intubation, rapid sequence induction intubation (RSII) with cricoid pressure (CP) is frequently implemented to prevent aspiration pneumonia. We evaluated the CVS in endotracheal intubation in RSII with CP, in comparison with a direct laryngoscope (DL). METHODS: One hundred fifty patients were randomly assigned to one of three groups: the CVS as a video stylet (CVS-V) group, the CVS as a lightwand (CVS-L) group and DL group. Primary outcomes were to assess the power of the CVS, compared with DL, regarding the first attempt success rate and intubation time in simulated RSII with CP. Secondary outcomes were to examine hemodynamic stress response and the incidence of complications. RESULTS: The first attempt success rates within 30 s and within 60 s were higher in CVS-V and DL group than those in CVS-L group (p = 0.006 and 0.037, respectively). The intergroup difference for intubation success rate within 30 s was nonsignificant and almost all the patients were successfully intubated within 60 s (98% for CVS-L and DL group, 96% for CVS-L group). Kaplan-Meier estimator demonstrated the median intubation time was 10.6 s [95% CI, 7.5 to 13.7] in CVS-V group, 14.6 s [95% CI, 11.1 to 18.0] in CVS-L group and 16.5 s [95% CI, 15.7 to 17.3] in DL group (p = 0.023 by the log-rank test). However, the difference was nonsignificant after Sidak's adjustment. The intergroup differences for hemodynamic stress response, sore throat and mucosa injury incidence were also nonsignificant. CONCLUSIONS: The CVS-D and DL provide a higher first attempt intubation success rate within 30 and 60 s in intubation with CP; the intubation time for the CVS-V was nonsignificantly shorter than that for the other two intubation methods. Almost all the patients can be successfully intubated with any of the three intubation methods within 60 s. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03841890 , registered on February 15, 2019 (retrospectively registered).

Defective GABAergic neurotransmission in the nucleus tractus solitarius in Mecp2-null mice, a model of Rett syndrome.

Rett syndrome (RTT) is a devastating neurodevelopmental disorder caused by loss-of-function mutations in the X-linked methyl-CpG binding protein 2 (Mecp2) gene. GABAergic dysfunction has been implicated contributing to the respiratory dysfunction, one major clinical feature of RTT. The nucleus tractus solitarius (NTS) is the first central site integrating respiratory sensory information that can change the nature of the reflex output. We hypothesized that deficiency in Mecp2 gene reduces GABAergic neurotransmission in the NTS. Using whole-cell patch-clamp recordings in NTS slices, we measured spontaneous inhibitory postsynaptic currents (sIPSCs), miniature IPSCs (mIPSCs), NTS-evoked IPSCs (eIPSCs), and GABAA receptor (GABAA-R) agonist-induced responses. Compared to those from wild-type mice, NTS neurons from Mecp2-null mice had significantly (p<0.05) reduced sIPSC amplitude, sIPSC frequency, and mIPSC amplitude but not mIPSC frequency. Mecp2-null mice also had decreased eIPSC amplitude with no change in paired-pulse ratio. The data suggest reduced synaptic receptor-mediated phasic GABA transmission in Mecp2-null mice. In contrast, muscimol (GABAA-R agonist, 0.3-100µM) and THIP (selective extrasynaptic GABAA-R agonist, 5µM) induced significantly greater current response in Mecp2-null mice, suggesting increased extrasynaptic receptors. Using qPCR, we found a 2.5 fold increase in the delta subunit of the GABAA-Rs in the NTS in Mecp2-null mice, consistent with increased extrasynaptic receptors. As the NTS was recently found required for respiratory pathology in RTT, our results provide a mechanism for NTS dysfunction which involves shifting the balance of synaptic/extrasynaptic receptors in favor of extrasynaptic site, providing a target for boosting GABAergic inhibition in RTT.

The Forebrain Thirst Circuit Drives Drinking through Negative Reinforcement.

The brain transforms the need for water into the desire to drink, but how this transformation is performed remains unknown. Here we describe the motivational mechanism by which the forebrain thirst circuit drives drinking. We show that thirst-promoting subfornical organ neurons are negatively reinforcing and that this negative-valence signal is transmitted along projections to the organum vasculosum of the lamina terminalis (OVLT) and median preoptic nucleus (MnPO). We then identify molecularly defined cell types within the OVLT and MnPO that are activated by fluid imbalance and show that stimulation of these neurons is sufficient to drive drinking, cardiovascular responses, and negative reinforcement. Finally, we demonstrate that the thirst signal exits these regions through at least three parallel pathways and show that these projections dissociate the cardiovascular and behavioral responses to fluid imbalance. These findings reveal a distributed thirst circuit that motivates drinking by the common mechanism of drive reduction.

Dynamics of Gut-Brain Communication Underlying Hunger.

Communication between the gut and brain is critical for homeostasis, but how this communication is represented in the dynamics of feeding circuits is unknown. Here we describe nutritional regulation of key neurons that control hunger in vivo. We show that intragastric nutrient infusion rapidly and durably inhibits hunger-promoting AgRP neurons in awake, behaving mice. This inhibition is proportional to the number of calories infused but surprisingly independent of macronutrient identity or nutritional state. We show that three gastrointestinal signals-serotonin, CCK, and PYY-are necessary or sufficient for these effects. In contrast, the hormone leptin has no acute effect on dynamics of these circuits or their sensory regulation but instead induces a slow modulation that develops over hours and is required for inhibition of feeding. These findings reveal how layers of visceral signals operating on distinct timescales converge on hypothalamic feeding circuits to generate a central representation of energy balance.

Rational Design of Membrane-Pore-Forming Peptides.

Specific interactions of peptides with lipid membranes are essential for cellular communication and constitute a central aspect of the innate host defense against pathogens. A computational method for generating innovative membrane-pore-forming peptides inspired by natural templates is presented. Peptide representation in terms of sequence- and topology-dependent hydrophobic moments is introduced. This design concept proves to be appropriate for the de novo generation of first-in-class membrane-active peptides with the anticipated mode of action. The designed peptides outperform the natural template in terms of their antibacterial activity. They form a kinked helical structure and self-assemble in the membrane by an entropy-driven mechanism to form dynamically growing pores that are dependent on the lipid composition. The results of this study demonstrate the unique potential of natural template-based peptide design for chemical biology and medicinal chemistry.

Warm-Sensitive Neurons that Control Body Temperature.

Thermoregulation is one of the most vital functions of the brain, but how temperature information is converted into homeostatic responses remains unknown. Here, we use an unbiased approach for activity-dependent RNA sequencing to identify warm-sensitive neurons (WSNs) within the preoptic hypothalamus that orchestrate the homeostatic response to heat. We show that these WSNs are molecularly defined by co-expression of the neuropeptides BDNF and PACAP. Optical recordings in awake, behaving mice reveal that these neurons are selectively activated by environmental warmth. Optogenetic excitation of WSNs triggers rapid hypothermia, mediated by reciprocal changes in heat production and loss, as well as dramatic cold-seeking behavior. Projection-specific manipulations demonstrate that these distinct effectors are controlled by anatomically segregated pathways. These findings reveal a molecularly defined cell type that coordinates the diverse behavioral and autonomic responses to heat. Identification of these warm-sensitive cells provides genetic access to the core neural circuit regulating the body temperature of mammals. PAPERCLIP.

Thirst neurons anticipate the homeostatic consequences of eating and drinking.

Thirst motivates animals to drink in order to maintain fluid balance. Thirst has conventionally been viewed as a homeostatic response to changes in blood volume or tonicity. However, most drinking behaviour is regulated too rapidly to be controlled by blood composition directly, and instead seems to anticipate homeostatic imbalances before they arise. How this is achieved remains unknown. Here we reveal an unexpected role for the subfornical organ (SFO) in the anticipatory regulation of thirst in mice. By monitoring deep-brain calcium dynamics, we show that thirst-promoting SFO neurons respond to inputs from the oral cavity during eating and drinking and then integrate these inputs with information about the composition of the blood. This integration allows SFO neurons to predict how ongoing food and water consumption will alter fluid balance in the future and then to adjust behaviour pre-emptively. Complementary optogenetic manipulations show that this anticipatory modulation is necessary for drinking in several contexts. These findings provide a neural mechanism to explain longstanding behavioural observations, including the prevalence of drinking during meals, the rapid satiation of thirst, and the fact that oral cooling is thirst-quenching.

Hunger neurons drive feeding through a sustained, positive reinforcement signal.

The neural mechanisms underlying hunger are poorly understood. AgRP neurons are activated by energy deficit and promote voracious food consumption, suggesting these cells may supply the fundamental hunger drive that motivates feeding. However recent in vivo recording experiments revealed that AgRP neurons are inhibited within seconds by the sensory detection of food, raising the question of how these cells can promote feeding at all. Here we resolve this paradox by showing that brief optogenetic stimulation of AgRP neurons before food availability promotes intense appetitive and consummatory behaviors that persist for tens of minutes in the absence of continued AgRP neuron activation. We show that these sustained behavioral responses are mediated by a long-lasting potentiation of the rewarding properties of food and that AgRP neuron activity is positively reinforcing. These findings reveal that hunger neurons drive feeding by transmitting a positive valence signal that triggers a stable transition between behavioral states.

Revisiting the Lamotrigine-Mediated Effect on Hippocampal GABAergic Transmission.

Lamotrigine (LTG) is generally considered as a voltage-gated sodium (Nav) channel blocker. However, recent studies suggest that LTG can also serve as a hyperpolarization-activated cyclic nucleotide-gated (HCN) channel enhancer and can increase the excitability of GABAergic interneurons (INs). Perisomatic inhibitory INs, predominantly fast-spiking basket cells (BCs), powerfully inhibit granule cells (GCs) in the hippocampal dentate gyrus. Notably, BCs express abundant Nav channels and HCN channels, both of which are able to support sustained action potential generation. Using whole-cell recording in rat hippocampal slices, we investigated the net LTG effect on BC output. We showed that bath application of LTG significantly decreased the amplitude of evoked compound inhibitory postsynaptic currents (IPSCs) in GCs. In contrast, simultaneous paired recordings from BCs to GCs showed that LTG had no effect on both the amplitude and the paired-pulse ratio of the unitary IPSCs, suggesting that LTG did not affect GABA release, though it suppressed cell excitability. In line with this, LTG decreased spontaneous IPSC (sIPSC) frequency, but not miniature IPSC frequency. When re-examining the LTG effect on GABAergic transmission in the cornus ammonis region 1 (CA1) area, we found that LTG markedly inhibits both the excitability of dendrite-targeting INs in the stratum oriens and the concurrent sIPSCs recorded on their targeting pyramidal cells (PCs) without significant hyperpolarization-activated current (Ih) enhancement. In summary, LTG has no effect on augmenting Ih in GABAergic INs and does not promote GABAergic inhibitory output. The antiepileptic effect of LTG is likely through Nav channel inhibition and the suppression of global neuronal network activity.

Multidimensional Design of Anticancer Peptides.

The computer-assisted design and optimization of peptides with selective cancer cell killing activity was achieved through merging the features of anticancer peptides, cell-penetrating peptides, and tumor-homing peptides. Machine-learning classifiers identified candidate peptides that possess the predicted properties. Starting from a template amino acid sequence, peptide cytotoxicity against a range of cancer cell lines was systematically optimized while minimizing the effects on primary human endothelial cells. The computer-generated sequences featured improved cancer-cell penetration, induced cancer-cell apoptosis, and were enabled a decrease in the cytotoxic concentration of co-administered chemotherapeutic agents in vitro. This study demonstrates the potential of multidimensional machine-learning methods for rapidly obtaining peptides with the desired cellular activities.