Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Analysis of the Human Kinome and Phosphatome by Mass Cytometry Reveals Overexpression-Induced Effects on Cancer-Related Signaling.

Kinase and phosphatase overexpression drives tumorigenesis and drug resistance. We previously developed a mass-cytometry-based single-cell proteomics approach that enables quantitative assessment of overexpression effects on cell signaling. Here, we applied this approach in a human kinome- and phosphatome-wide study to assess how 649 individually overexpressed proteins modulated cancer-related signaling in HEK293T cells in an abundance-dependent manner. Based on these data, we expanded the functional classification of human kinases and phosphatases and showed that the overexpression effects include non-catalytic roles. We detected 208 previously unreported signaling relationships. The signaling dynamics analysis indicated that the overexpression of ERK-specific phosphatases sustains proliferative signaling. This suggests a phosphatase-driven mechanism of cancer progression. Moreover, our analysis revealed a drug-resistant mechanism through which overexpression of tyrosine kinases, including SRC, FES, YES1, and BLK, induced MEK-independent ERK activation in melanoma A375 cells. These proteins could predict drug sensitivity to BRAF-MEK concurrent inhibition in cells carrying BRAF mutations.

Collaborative imagination synchronizes representations of the future and fosters social connection in the present.

From close friends to people on a first date, imagining a shared future appears fundamental to relationships. Yet, no previous research has conceptualized the act of imagination as a socially constructed process that affects how connected we feel to others. The present studies provide a framework for investigating imagination as a collaborative process in which individuals cocreate shared representations of hypothetical events-what we call collaborative imagination. Across two preregistered studies (N = 244), we provide evidence that collaborative imagination of a shared future fosters social connection in novel dyads-beyond imagining a shared future individually or shared experience in general. Subjective ratings and natural language processing of participants' imagined narratives illuminate the representational features of imagined events shaped by collaborative imagination. Together, the present findings have the potential to shift how we view the structure and function of imagination with implications for better understanding interpersonal relationships and collective cognition.

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots.

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Centering relationships to place for more meaningful research and engagement.

Research has the potential to simultaneously generate new knowledge and contribute meaningful social-ecological benefits; however, research processes and outcomes can also perpetuate extractive patterns that have manifested the climate, biodiversity, and social justice crises. One approach to enhance the societal value of research processes is to strengthen relationships with places of study and the peoples of those places. Deepening relational engagement with the social-ecological context and history of a place can lead to more accurate results and improved public trust in the scientific process and is particularly important for natural scientists who work at the interface of nature and society. We provide three actionable pathways that range from individual to systemic change to enhance place-based relationships within research systems: 1) deepen reflection and communication about relationships with places and peoples; 2) strengthen collaboration among research teams and partners; and 3) transform systems of knowledge creation to foster place-based roots. Action on any of these proposed pathways, but especially action taken across all three, can build empathy and connections to place and people, strengthening the meaningful impact of research both locally and globally.

From covalent transition states in chemistry to noncovalent in biology: from ß- to Φ-value analysis of protein folding.

Solving the mechanism of a chemical reaction requires determining the structures of all the ground states on the pathway and the elusive transition states linking them. 2024 is the centenary of Brønsted's landmark paper that introduced the ß-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium-free energies. Protein engineering was introduced for an analogous procedure, Φ-value analysis, to analyse the noncovalent interactions in proteins central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis. The mature procedure was then applied to study transition states in the pathway of protein folding - 'part (b) of the protein folding problem'. This review describes the development of Φ-value analysis of transition states and compares and contrasts the interpretation of ß- and Φ-values and their limitations. Φ-analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ-analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ-values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.

High-throughput experimentation for discovery of biodegradable polyesters.

The consistent rise of plastic pollution has stimulated interest in the development of biodegradable plastics. However, the study of polymer biodegradation has historically been limited to a small number of polymers due to costly and slow standard methods for measuring degradation, slowing new material innovation. High-throughput polymer synthesis and a high-throughput polymer biodegradation method are developed and applied to generate a biodegradation dataset for 642 chemically distinct polyesters and polycarbonates. The biodegradation assay was based on the clear-zone technique, using automation to optically observe the degradation of suspended polymer particles under the action of a single Pseudomonas lemoignei bacterial colony. Biodegradability was found to depend strongly on aliphatic repeat unit length, with chains less than 15 carbons and short side chains improving biodegradability. Aromatic backbone groups were generally detrimental to biodegradability; however, ortho- and para-substituted benzene rings in the backbone were more likely to be degradable than metasubstituted rings. Additionally, backbone ether groups improved biodegradability. While other heteroatoms did not show a clear improvement in biodegradability, they did demonstrate increases in biodegradation rates. Machine learning (ML) models were leveraged to predict biodegradability on this large dataset with accuracies over 82% using only chemical structure descriptors.

Controlling swelling in mixed transport polymers through alkyl side-chain physical cross-linking.

Semiconducting conjugated polymers bearing glycol side chains can simultaneously transport both electronic and ionic charges with high charge mobilities, making them ideal electrode materials for a range of bioelectronic devices. However, heavily glycolated conjugated polymer films have been observed to swell irreversibly when subjected to an electrochemical bias in an aqueous electrolyte. The excessive swelling can lead to the degradation of their microstructure, and subsequently reduced device performance. An effective strategy to control polymer film swelling is to copolymerize glycolated repeat units with a fraction of monomers bearing alkyl side chains, although the microscopic mechanism that constrains swelling is unknown. Here we investigate, experimentally and computationally, a series of archetypal mixed transporting copolymers with varying ratios of glycolated and alkylated repeat units. Experimentally we observe that exchanging 10% of the glycol side chains for alkyl leads to significantly reduced film swelling and an increase in electrochemical stability. Through molecular dynamics simulation of the amorphous phase of the materials, we observe the formation of polymer networks mediated by alkyl side-chain interactions. When in the presence of water, the network becomes increasingly connected, counteracting the volumetric expansion of the polymer film.

Bonobos and chimpanzees remember familiar conspecifics for decades.

Recognition and memory of familiar conspecifics provides the foundation for complex sociality and is vital to navigating an unpredictable social world [Tibbetts and Dale, Trends Ecol. Evol. 22, 529-537 (2007)]. Human social memory incorporates content about interactions and relationships and can last for decades [Sherry and Schacter, Psychol. Rev. 94, 439-454 (1987)]. Long-term social memory likely played a key role throughout human evolution, as our ancestors increasingly built relationships that operated across distant space and time [Malone et al., Int. J. Primatol. 33, 1251-1277 (2012)]. Although individual recognition is widespread among animals and sometimes lasts for years, little is known about social memory in nonhuman apes and the shared evolutionary foundations of human social memory. In a preferential-looking eye-tracking task, we presented chimpanzees and bonobos (N = 26) with side-by-side images of a previous groupmate and a conspecific stranger of the same sex. Apes' attention was biased toward former groupmates, indicating long-term memory for past social partners. The strength of biases toward former groupmates was not impacted by the duration apart, and our results suggest that recognition may persist for at least 26 y beyond separation. We also found significant but weak evidence that, like humans, apes may remember the quality or content of these past relationships: apes' looking biases were stronger for individuals with whom they had more positive histories of social interaction. Long-lasting social memory likely provided key foundations for the evolution of human culture and sociality as they extended across time, space, and group boundaries.

A Tour of TOR Complex Signaling in Plants.

To identify the appropriate times for growth and development, organisms must sense and process information about the availability of nutrients, energy status, and environmental cues. For sessile eukaryotes such as plants, integrating such information can be critical in life or death decisions. For nearly 30 years, the conserved phosphatidylinositol 3-kinase-related protein kinases (PIKKs) target of rapamycin (TOR) has been established as a central hub for integrating external and internal metabolic cues. Despite the functional conservation across eukaryotes, the TOR complex has evolved specific functional and mechanistic features in plants. Here, we present recent findings on the plant TOR complex that highlight the conserved and unique nature of this critical growth regulator and its role in multiple aspects of plant life.

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA.

RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

CMGN: a conditional molecular generation net to design target-specific molecules with desired properties.

The rational design of chemical entities with desired properties for a specific target is a long-standing challenge in drug design. Generative neural networks have emerged as a powerful approach to sample novel molecules with specific properties, termed as inverse drug design. However, generating molecules with biological activity against certain targets and predefined drug properties still remains challenging. Here, we propose a conditional molecular generation net (CMGN), the backbone of which is a bidirectional and autoregressive transformer. CMGN applies large-scale pretraining for molecular understanding and navigates the chemical space for specified targets by fine-tuning with corresponding datasets. Additionally, fragments and properties were trained to recover molecules to learn the structure-properties relationships. Our model crisscrosses the chemical space for specific targets and properties that control fragment-growth processes. Case studies demonstrated the advantages and utility of our model in fragment-to-lead processes and multi-objective lead optimization. The results presented in this paper illustrate that CMGN has the potential to accelerate the drug discovery process.

Thalamic epileptic spikes disrupt sleep spindles in patients with epileptic encephalopathy.

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-REM sleep (EE-SWAS), a sleep stage dominated by sleep spindles, which are brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep-activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, P ≈ 0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (P ≈ 0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 s after each thalamic spike (P < 0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep.

Favoritism or bias? Cooperation and competition under different intergroup relationships: evidence from EEG hyperscanning.

Cooperation and competition are the most common forms of social interaction in various social relationships. Intergroup relationships have been posited to influence individuals' interpersonal interactions significantly. Using electroencephalography hyperscanning, this study aimed to establish whether intergroup relationships influence interpersonal cooperation and competition and the underlying neural mechanisms. According to the results, the in-group Coop-index is better than the out-group, whereas the out-group Comp-index is stronger than the in-group. The in-group functional connectivity between the frontal-central region and the right temporoparietal junction in the ß band was stronger in competition than cooperation. The out-group functional connectivity between the frontal-central region and the left temporoparietal junction in the α band was stronger in cooperation than competition. In both cooperation and competition, the in-group exhibited higher interbrain synchronization between the prefrontal cortex and parietal region in the θ band, as well as between the frontal-central region and frontal-central region in the α band, compared to the out-group. The intrabrain phase-locking value in both the α and ß bands can effectively predict performance in competition tasks. Interbrain phase-locking value in both the α and θ bands can be effectively predicted in a performance cooperation task. This study offers neuroscientific evidence for in-group favoritism and out-group bias at an interpersonal level.

Six types of loves differentially recruit reward and social cognition brain areas.

Feelings of love are among the most significant human phenomena. Love informs the formation and maintenance of pair bonds, parent-offspring attachments, and influences relationships with others and even nature. However, little is known about the neural mechanisms of love beyond romantic and maternal types. Here, we characterize the brain areas involved in love for six different objects: romantic partner, one's children, friends, strangers, pets, and nature. We used functional magnetic resonance imaging (fMRI) to measure brain activity, while we induced feelings of love using short stories. Our results show that neural activity during a feeling of love depends on its object. Interpersonal love recruited social cognition brain areas in the temporoparietal junction and midline structures significantly more than love for pets or nature. In pet owners, love for pets activated these same regions significantly more than in participants without pets. Love in closer affiliative bonds was associated with significantly stronger and more widespread activation in the brain's reward system than love for strangers, pets, or nature. We suggest that the experience of love is shaped by both biological and cultural factors, originating from fundamental neurobiological mechanisms of attachment.

Romantic relationships attenuated competition between lovers: evidence from brain synchronization.

Competition is an essential component of social interaction and is influenced by interpersonal relationships. This study is based on social exchange theory and explores the relationship between brain synchronization and competition in the binary system of romantic relationships through electroencephalogram hyperscanning technology. The results found that females had a greater win rate in the romantic and friend groups. During the early stage (0-200 ms), when the competitive target appeared, the stranger group exhibited greater interbrain synchronicity in the Alpha frequency band. However, during the later stage (600-800 ms), the romantic group showed higher Alpha band interbrain synchrony when the competitive target appeared. Significant interbrain synchronizations were observed in the Theta frequency band of the stranger and friend groups at 400-600 ms and 800-1000 ms. Moreover, these interbrain synchronizations were significantly positively correlated with the winning rates of females in the competition. These findings suggest a close relationship between interpersonal coordination and interbrain synchronization. Furthermore, romantic relationships reduce participants' willingness to compete, affecting their attention regulation, emotional processing, and goal orientation, thus influencing competition. This study investigated the impact of romantic relationships on competition, providing a theoretical foundation for promoting the positive and healthy development of romantic relationships.

Intimate relationships regulate female brain activity in a competitive context: evidence from EEG and functional connectivity analysis.

Competition is common in life, and intimate relationships are essential. Understanding how intimate relationships impact an individual's competitive process is crucial. This study explored the impact of competitor gender on female competition using electroencephalography analysis. The results revealed that females exhibited a smaller median of the absolute value of reaction time difference (DRT) between their partners and their competitors when their partners were absent compared to when their partners were present. Additionally, females showed greater average amplitudes of N2 posterior contralateral component (N2pc) and Late Positive Potential (LPP), increased activation of the alpha frequency band, and enhanced theta frequency band functional connectivity between the central parietal lobe and occipital lobe. Furthermore, when competing with individuals of the same gender as opposed to individuals of the opposite gender, females exhibited greater average amplitudes of percentage of wins and N2pc. A significant negative correlation was noted between the DRT and the average wave amplitudes of N2pc and LPP. These findings suggest that females are more engaged in competitive tasks when partners are not present and have improved decision-making when competing with same-gender individuals. This study provides evidence for the influence of lovers on female competition, helping females adapt to social competition and promoting healthy relationships.

Parenting links to parent-child interbrain synchrony: a real-time fNIRS hyperscanning study.

Parent-child interaction is crucial for children's cognitive and affective development. While bio-synchrony models propose that parenting influences interbrain synchrony during interpersonal interaction, the brain-to-brain mechanisms underlying real-time parent-child interactions remain largely understudied. Using functional near-infrared spectroscopy, we investigated interbrain synchrony in 88 parent-child dyads (Mage children = 8.07, 42.0% girls) during a collaborative task (the Etch-a-Sketch, a joint drawing task). Our findings revealed increased interbrain synchrony in the dorsolateral prefrontal cortex and temporo-parietal areas during interactive, collaborative sessions compared to non-interactive, resting sessions. Linear regression analysis demonstrated that interbrain synchrony in the left temporoparietal junction was associated with enhanced dyadic collaboration, shared positive affect, parental autonomy support, and parental emotional warmth. These associations remained significant after controlling for demographic variables including child age, child gender, and parent gender. Additionally, differences between fathers and mothers were observed. These results highlight the significant association between brain-to-brain synchrony in parent-child dyads, the quality of the parent-child relationship, and supportive parenting behaviors. Interbrain synchrony may serve as a neurobiological marker of real-time parent-child interaction, potentially underscoring the pivotal role of supportive parenting in shaping these interbrain synchrony mechanisms.

Pursuing Safety in Social Connection: A Flexibly Fluid Perspective on Risk Regulation in Relationships.

People are fundamentally motivated to be included in social connections that feel safe, connections where they are consistently cared for and protected, not hurt or exploited. Romantic relationships have long played a crucial role in satisfying this fundamental need. This article reconceptualizes the risk-regulation model to argue that people draw on experiences from inside and outside their romantic relationships to satisfy their fundamental need to feel safe depending on others. We first review the direct relational cues (i.e., a partner's affectionate touch, responsive versus unresponsive behavior, and relative power) and indirect cues (i.e., bodily sensations, collective value in the eyes of others, and living conditions) that signal the current safety of social connection and motivate people to connect to others or protect themselves against them. We then review how people's chronic capacity to trust in others controls their sensitivity and reactivity to the safety cues. The article concludes with future research directions.

Structural Premise of Selective Deubiquitinase USP30 Inhibition by Small-Molecule Benzosulfonamides.

Dampening functional levels of the mitochondrial deubiquitylating enzyme Ubiquitin-specific protease 30 (USP30) has been suggested as an effective therapeutic strategy against neurodegenerative disorders such as Parkinson's Disease. USP30 inhibition may counteract the deleterious effects of impaired turnover of damaged mitochondria, which is inherent to both familial and sporadic forms of the disease. Small-molecule inhibitors targeting USP30 are currently in development, but little is known about their precise nature of binding to the protein. We have integrated biochemical and structural approaches to gain novel mechanistic insights into USP30 inhibition by a small-molecule benzosulfonamide-containing compound, USP30inh. Activity-based protein profiling mass spectrometry confirmed target engagement, high selectivity, and potency of USP30inh for USP30 against 49 other deubiquitylating enzymes in a neuroblastoma cell line. In vitro characterization of USP30inh enzyme kinetics inferred slow and tight binding behavior, which is comparable with features of covalent modification of USP30. Finally, we blended hydrogen-deuterium exchange mass spectrometry and computational docking to elucidate the molecular architecture and geometry of USP30 complex formation with USP30inh, identifying structural rearrangements at the cleft of the USP30 thumb and palm subdomains. These studies suggest that USP30inh binds to this thumb-palm cleft, which guides the ubiquitin C terminus into the active site, thereby preventing ubiquitin binding and isopeptide bond cleavage, and confirming its importance in the inhibitory process. Our data will pave the way for the design and development of next-generation inhibitors targeting USP30 and associated deubiquitinylases.