Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

PROTAX-GPU: a scalable probabilistic taxonomic classification system for DNA barcodes

DNA-based identification is vital for classifying biological specimens, yet methods to quantify the uncertainty of sequence-based taxonomic assignments are scarce. Challenges arise from noisy reference databases, including mislabelled entries and missing taxa. PROTAX addresses these issues with a probabilistic approach to taxonomic classification, advancing on methods that rely solely on sequence similarity. It provides calibrated probabilistic assignments to a partially populated taxonomic hierarchy, accounting for taxa that lack references and incorrect taxonomic annotation. While effective on smaller scales, global application of PROTAX necessitates substantially larger reference libraries, a goal previously hindered by computational barriers. We introduce PROTAX-GPU, a scalable algorithm capable of leveraging the global Barcode of Life Data System (>14 million specimens) as a reference database. Using graphics processing units (GPU) to accelerate similarity and nearest-neighbour operations and the JAX library for Python integration, we achieve over a 1000 × speedup compared with the central processing unit (CPU)-based implementation without compromising PROTAX's key benefits. PROTAX-GPU marks a significant stride towards real-time DNA barcoding, enabling quicker and more efficient species identification in environmental assessments. This capability opens up new avenues for real-time monitoring and analysis of biodiversity, advancing our ability to understand and respond to ecological dynamics. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Diet, Pace of Biological Aging, and Risk of Dementia in the Framingham Heart Study

OBJECTIVE

People who eat healthier diets are less likely to develop dementia, but the biological mechanism of this protection is not well understood. We tested the hypothesis that healthy diet protects against dementia because it slows the pace of biological aging.

METHODS

We analyzed Framingham Offspring Cohort data. We included participants ≥60 years-old, free of dementia and having dietary, epigenetic, and follow-up data. We assessed healthy diet as long-term adherence to the Mediterranean-Dash Intervention for Neurodegenerative Delay diet (MIND, over 4 visits spanning 1991-2008). We measured the pace of aging from blood DNA methylation data collected in 2005-2008 using the DunedinPACE epigenetic clock. Incident dementia and mortality were defined using study records compiled from 2005 to 2008 visit through 2018.

RESULTS

Of n = 1,644 included participants (mean age 69.6, 54% female), n = 140 developed dementia and n = 471 died over 14 years of follow-up. Greater MIND score was associated with slower DunedinPACE and reduced risks for dementia and mortality. Slower DunedinPACE was associated with reduced risks for dementia and mortality. In mediation analysis, slower DunedinPACE accounted for 27% of the diet-dementia association and 57% of the diet-mortality association.

INTERPRETATION

Findings suggest that slower pace of aging mediates part of the relationship of healthy diet with reduced dementia risk. Monitoring pace of aging may inform dementia prevention. However, a large fraction of the diet-dementia association remains unexplained and may reflect direct connections between diet and brain aging that do not overlap other organ systems. Investigation of brain-specific mechanisms in well-designed mediation studies is warranted. ANN NEUROL 2024;95:1069-1079.

Novel technologies uncover novel 'anti'-microbial peptides in Hydra shaping the species-specific microbiome

The freshwater polyp Hydra uses an elaborate innate immune machinery to maintain its specific microbiome. Major components of this toolkit are conserved Toll-like receptor (TLR)-mediated immune pathways and species-specific antimicrobial peptides (AMPs). Our study harnesses advanced technologies, such as high-throughput sequencing and machine learning, to uncover a high complexity of the Hydra's AMPs repertoire. Functional analysis reveals that these AMPs are specific against diverse members of the Hydra microbiome and expressed in a spatially controlled pattern. Notably, in the outer epithelial layer, AMPs are produced mainly in the neurons. The neuron-derived AMPs are secreted directly into the glycocalyx, the habitat for symbiotic bacteria, and display high selectivity and spatial restriction of expression. In the endodermal layer, in contrast, endodermal epithelial cells produce an abundance of different AMPs including members of the arminin and hydramacin families, while gland cells secrete kazal-type protease inhibitors. Since the endodermal layer lines the gastric cavity devoid of symbiotic bacteria, we assume that endodermally secreted AMPs protect the gastric cavity from intruding pathogens. In conclusion, Hydra employs a complex set of AMPs expressed in distinct tissue layers and cell types to combat pathogens and to maintain a stable spatially organized microbiome. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

A new lexicon in the age of microbiome research

At a rapid pace, biologists are learning the many ways in which resident microbes influence, and sometimes even control, their hosts to shape both health and disease. Understanding the biochemistry behind these interactions promises to reveal completely novel and targeted ways of counteracting disease processes. However, in our protocols and publications, we continue to describe these new results using a language that originated in a completely different context. This language developed when microbial interactions with hosts were perceived to be primarily pathogenic, as threats that had to be vanquished. Biomedicine had one dominating thought: winning this war against microorganisms. Today, we know that beyond their defensive roles, host tissues, especially epithelia, are vital to ensuring association with the normal microbiota, the communities of microbes that persistently live with the host. Thus, we need to adopt a language that better encompasses the newly appreciated importance of host-microbiota associations. We also need a language that frames the onset and progression of pathogenic conditions within the context of the normal microbiota. Such a reimagined lexicon should make it clear, from the very nature of its words, that microorganisms are primarily vital to our health, and only more rarely the cause of disease. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

Highly Efficient Carbon Dioxide Electroreduction via DNA-Directed Catalyst Immobilization

Electrochemical reduction of carbon dioxide (CO2) is a promising route to up-convert this industrial byproduct. However, to perform this reaction with a small-molecule catalyst, the catalyst must be proximal to an electrode surface. Efforts to immobilize molecular catalysts on electrodes have been stymied by the need to optimize the immobilization chemistries on a case-by-case basis. Taking inspiration from nature, we applied DNA as a molecular-scale "Velcro" to investigate the tethering of three porphyrin-based catalysts to electrodes. This tethering strategy improved both the stability of the catalysts and their Faradaic efficiencies (FEs). DNA-catalyst conjugates were immobilized on screen-printed carbon and carbon paper electrodes via DNA hybridization with nearly 100% efficiency. Following immobilization, a higher catalyst stability at relevant potentials is observed. Additionally, lower overpotentials are required for the generation of carbon monoxide (CO). Finally, high FE for CO generation was observed with the DNA-immobilized catalysts as compared to the unmodified small-molecule systems, as high as 79.1% FE for CO at -0.95 V vs SHE using a DNA-tethered catalyst. This work demonstrates the potential of DNA "Velcro" as a powerful strategy for catalyst immobilization. Here, we demonstrated improved catalytic characteristics of molecular catalysts for CO2 valorization, but this strategy is anticipated to be generalizable to any reaction that proceeds in aqueous solutions.

Brain signal variability and executive functions across the life span

Neural variability is thought to facilitate survival through flexible adaptation to changing environmental demands. In humans, such capacity for flexible adaptation may manifest as fluid reasoning, inhibition of automatic responses, and mental set-switching-skills falling under the broad domain of executive functions that fluctuate over the life span. Neural variability can be quantified via the BOLD signal in resting-state fMRI. Variability of large-scale brain networks is posited to underpin complex cognitive activities requiring interactions between multiple brain regions. Few studies have examined the extent to which network-level brain signal variability across the life span maps onto high-level processes under the umbrella of executive functions. The present study leveraged a large publicly available neuroimaging dataset to investigate the relationship between signal variability and executive functions across the life span. Associations between brain signal variability and executive functions shifted as a function of age. Limbic-specific variability was consistently associated with greater performance across subcomponents of executive functions. Associations between executive function subcomponents and network-level variability of the default mode and central executive networks, as well as whole-brain variability, varied across the life span. Findings suggest that brain signal variability may help to explain to age-related differences in executive functions across the life span.

The evosystem: A centerpiece for evolutionary studies

In this paper, we redefine the target of evolutionary explanations by proposing the "evosystem" as an alternative to populations, lineages and species. Evosystems account for changes in the distribution of heritable variation within individual Darwinian populations (evolution by natural selection, drift, or constructive neutral evolution), but also for changes in the networks of interactions within or between Darwinian populations and changes in the abiotic environment (whether these changes are caused by the organic entities or not). The evosystem can thereby become a centerpiece for a redefined evolutionary science, that is, evolutionary studies, that apprehends through a single framework the variety of evolutionary processes that lie at various scales. To illustrate the importance of this broadened perspective on evolution, we use a case of antimicrobial resistance evolution: the spread of the blaNDM gene family and the related resistance to carbapenem antibiotics observed globally, and show how evolutionary studies can contribute to answering contemporary socially relevant challenges.

Ultrafast Computational Screening of Molecules with Inverted Singlet-Triplet Energy Gaps Using the Pariser-Parr-Pople Semiempirical Quantum Chemistry Method

Molecules with an inverted energy gap between their first singlet and triplet excited states have promising applications in the next generation of organic light-emitting diode (OLED) materials. Unfortunately, such molecules are rare, and only a handful of examples are currently known. High-throughput virtual screening could assist in finding novel classes of these molecules, but current efforts are hampered by the high computational cost of the required quantum chemical methods. We present a method based on the semiempirical Pariser-Parr-Pople theory augmented by perturbation theory and show that it reproduces inverted gaps at a fraction of the cost of currently employed excited-state calculations. Our study paves the way for ultrahigh-throughput virtual screening and inverse design to accelerate the discovery and development of this new generation of OLED materials.

Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network

Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion analysis, and media studies. In such areas, it is essential to have easily interpretable, diverse, and coherent topics. An efficient topic modeling technique should accurately identify flat and hierarchical topics, especially useful in disciplines where topics can be logically arranged into a tree format. In this paper, we propose Community Topic, a novel algorithm that exploits word co-occurrence networks to mine communities and produces topics. We also evaluate the proposed approach using several metrics and compare it with usual baselines, confirming its good performances. Community Topic enables quick identification of flat topics and topic hierarchy, facilitating the on-demand exploration of sub- and super-topics. It also obtains good results on datasets in different languages.

Spectrotemporal cues and attention jointly modulate fMRI network topology for sentence and melody perception

Speech and music are two fundamental modes of human communication. Lateralisation of key processes underlying their perception has been related both to the distinct sensitivity to low-level spectrotemporal acoustic features and to top-down attention. However, the interplay between bottom-up and top-down processes needs to be clarified. In the present study, we investigated the contribution of acoustics and attention to melodies or sentences to lateralisation in fMRI functional network topology. We used sung speech stimuli selectively filtered in temporal or spectral modulation domains with crossed and balanced verbal and melodic content. Perception of speech decreased with degradation of temporal information, whereas perception of melodies decreased with spectral degradation. Applying graph theoretical metrics on fMRI connectivity matrices, we found that local clustering, reflecting functional specialisation, linearly increased when spectral or temporal cues crucial for the task goal were incrementally degraded. These effects occurred in a bilateral fronto-temporo-parietal network for processing temporally degraded sentences and in right auditory regions for processing spectrally degraded melodies. In contrast, global topology remained stable across conditions. These findings suggest that lateralisation for speech and music partially depends on an interplay of acoustic cues and task goals under increased attentional demands.

Dissociations between spontaneous electroencephalographic features and the perturbational complexity index in the minimally conscious state

The analysis of spontaneous electroencephalogram (EEG) is a cornerstone in the assessment of patients with disorders of consciousness (DoC). Although preserved EEG patterns are highly suggestive of consciousness even in unresponsive patients, moderately or severely abnormal patterns are difficult to interpret. Indeed, growing evidence shows that consciousness can be present despite either large delta or reduced alpha activity in spontaneous EEG. Quantifying the complexity of EEG responses to direct cortical perturbations (perturbational complexity index [PCI]) may complement the observational approach and provide a reliable assessment of consciousness even when spontaneous EEG features are inconclusive. To seek empirical evidence of this hypothesis, we compared PCI with EEG spectral measures in the same population of minimally conscious state (MCS) patients (n = 40) hospitalized in rehabilitation facilities. We found a remarkable variability in spontaneous EEG features across MCS patients as compared with healthy controls: in particular, a pattern of predominant delta and highly reduced alpha power-more often observed in vegetative state/unresponsive wakefulness syndrome (VS/UWS) patients-was found in a non-negligible number of MCS patients. Conversely, PCI values invariably fell above an externally validated empirical cutoff for consciousness in all MCS patients, consistent with the presence of clearly discernible, albeit fleeting, behavioural signs of awareness. These results confirm that, in some MCS patients, spontaneous EEG rhythms may be inconclusive about the actual capacity for consciousness and suggest that a perturbational approach can effectively compensate for this pitfall with practical implications for the individual patient's stratification and tailored rehabilitation.

Hydrogeological controls on microbial activity and habitability in the Precambrian continental crust

Earth's deep continental subsurface is a prime setting to study the limits of life's relationship with environmental conditions and habitability. In Precambrian crystalline rocks worldwide, deep ancient groundwaters in fracture networks are typically oligotrophic, highly saline, and locally inhabited by low-biomass communities in which chemolithotrophic microorganisms may dominate. Periodic opening of new fractures can lead to penetration of surface water and/or migration of fracture fluids, both of which may trigger changes in subsurface microbial composition and activity. These hydrogeological processes and their impacts on subsurface communities may play a significant role in global cycles of key elements in the crust. However, to date, considerable uncertainty remains on how subsurface microbial communities may respond to these changes in hydrogeochemical conditions. To address this uncertainty, the biogeochemistry of Thompson mine (Manitoba, Canada) was investigated. Compositional and isotopic analyses of fracture waters collected here at ~1 km below land surface revealed different extents of mixing between subsurface brine and (paleo)meteoric waters. To investigate the effects this mixing may have had on microbial communities, the Most Probable Number technique was applied to test community response for a total of 13 different metabolisms. The results showed that all fracture waters were dominated by viable heterotrophic microorganisms which can utilize organic materials associated with aerobic/facultative anaerobic processes, sulfate reduction, or fermentation. Where mixing between subsurface brines and (paleo)meteoric waters occurs, the communities demonstrate higher cell densities and increased viable functional potentials, compared to the most saline sample. This study therefore highlights the connection between hydrogeologic heterogeneity and the heterogeneity of subsurface ecosystems in the crystalline rocks, and suggests that hydrogeology can have a considerable impact on the scope and scale of subsurface microbial communities on Earth and potentially beyond.

DNA methylation-environment interactions in the human genome

Previously, we showed that a massively parallel reporter assay, mSTARR-seq, could be used to simultaneously test for both enhancer-like activity and DNA methylation-dependent enhancer activity for millions of loci in a single experiment (Lea et al., 2018). Here, we apply mSTARR-seq to query nearly the entire human genome, including almost all CpG sites profiled either on the commonly used Illumina Infinium MethylationEPIC array or via reduced representation bisulfite sequencing. We show that fragments containing these sites are enriched for regulatory capacity, and that methylation-dependent regulatory activity is in turn sensitive to the cellular environment. In particular, regulatory responses to interferon alpha (IFNA) stimulation are strongly attenuated by methyl marks, indicating widespread DNA methylation-environment interactions. In agreement, methylation-dependent responses to IFNA identified via mSTARR-seq predict methylation-dependent transcriptional responses to challenge with influenza virus in human macrophages. Our observations support the idea that pre-existing DNA methylation patterns can influence the response to subsequent environmental exposures-one of the tenets of biological embedding. However, we also find that, on average, sites previously associated with early life adversity are not more likely to functionally influence gene regulation than expected by chance.

Penalized canonical correlation analysis reveals a relationship between temperament traits and brain oscillations during mind wandering

INTRODUCTION

There has been a growing interest in studying brain activity under naturalistic conditions. However, the relationship between individual differences in ongoing brain activity and psychological characteristics is not well understood. We investigated this connection, focusing on the association between oscillatory activity in the brain and individually characteristic dispositional traits. Given the variability of unconstrained resting states among individuals, we devised a paradigm that could harmonize the state of mind across all participants.

METHODS

We constructed task contrasts that included focused attention (FA), self-centered future planning, and rumination on anxious thoughts triggered by visual imagery. Magnetoencephalography was recorded from 28 participants under these 3 conditions for a duration of 16 min. The oscillatory power in the alpha and beta bands was converted into spatial contrast maps, representing the difference in brain oscillation power between the two conditions. We performed permutation cluster tests on these spatial contrast maps. Additionally, we applied penalized canonical correlation analysis (CCA) to study the relationship between brain oscillation patterns and behavioral traits.

RESULTS

The data revealed that the FA condition, as compared to the other conditions, was associated with higher alpha and beta power in the temporal areas of the left hemisphere and lower alpha and beta power in the parietal areas of the right hemisphere. Interestingly, the penalized CCA indicated that behavioral inhibition was positively correlated, whereas anxiety was negatively correlated, with a pattern of high oscillatory power in the bilateral precuneus and low power in the bilateral temporal regions. This unique association was found in the anxious-thoughts condition when contrasted with the focused-attention condition.

CONCLUSION

Our findings suggest individual temperament traits significantly affect brain engagement in naturalistic conditions. This research underscores the importance of considering individual traits in neuroscience and offers an effective method for analyzing brain activity and psychological differences.

DeepVelo: deep learning extends RNA velocity to multi-lineage systems with cell-specific kinetics

Existing RNA velocity estimation methods strongly rely on predefined dynamics and cell-agnostic constant transcriptional kinetic rates, assumptions often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. Using a graph convolution network, DeepVelo overcomes these limitations by generalizing RNA velocity to cell populations containing time-dependent kinetics and multiple lineages. DeepVelo infers time-varying cellular rates of transcription, splicing, and degradation, recovers each cell's stage in the differentiation process, and detects functionally relevant driver genes regulating these processes. Application to various developmental and pathogenic processes demonstrates DeepVelo's capacity to study complex differentiation and lineage decision events in heterogeneous scRNA-seq data.

Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture

Assessing gaze behavior during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make gaze analysis difficult. Current approaches involve laborious coding of pupil positions. In settings where motion capture and mobile eye tracking are used concurrently in naturalistic tasks, it is critical that data collection be simple, efficient, and systematic. One solution is to combine eye tracking with motion capture to generate 3D gaze vectors. When combined with tracked or known object locations, 3D gaze vector generation can be automated. Here we use combined eye and motion capture and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three pupil data inputs: the efficacy of calibration routines was assessed, a validation task requiring short fixations on task-relevant locations, and a naturalistic object interaction task to bridge the gap between laboratory and "in the wild" studies. Further, we generated and compared models using spherical and Cartesian coordinate systems and monocular (left or right) or binocular data. All calibration routines performed similarly, with the best performance (i.e., sub-centimeter errors) coming from the naturalistic task trials when the participant is looking at an object in front of them. We found that spherical coordinate systems generate the most accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend 1-min calibration routines using binocular pupil data combined with a spherical world coordinate system to produce the highest-quality gaze vectors.

Commensal bacteria promote type I interferon signaling to maintain immune tolerance in mice

Type I interferons (IFNs) exert a broad range of biological effects important in coordinating immune responses, which have classically been studied in the context of pathogen clearance. Yet, whether immunomodulatory bacteria operate through IFN pathways to support intestinal immune tolerance remains elusive. Here, we reveal that the commensal bacterium, Bacteroides fragilis, utilizes canonical antiviral pathways to modulate intestinal dendritic cells (DCs) and regulatory T cell (Treg) responses. Specifically, IFN signaling is required for commensal-induced tolerance as IFNAR1-deficient DCs display blunted IL-10 and IL-27 production in response to B. fragilis. We further establish that IFN-driven IL-27 in DCs is critical in shaping the ensuing Foxp3+ Treg via IL-27Rα signaling. Consistent with these findings, single-cell RNA sequencing of gut Tregs demonstrated that colonization with B. fragilis promotes a distinct IFN gene signature in Foxp3+ Tregs during intestinal inflammation. Altogether, our findings demonstrate a critical role of commensal-mediated immune tolerance via tonic type I IFN signaling.

Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science but also a very difficult one due to the vast number of possible molecular systems. We propose an evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favorable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely, minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap; optimization was carried out over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) data sets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus feasible only thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches.

Cohort profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G)

The German Socio-Economic Panel (SOEP) serves a global research community by providing representative annual longitudinal data of respondents living in private households in Germany. The dataset offers a valuable life course panorama, encompassing living conditions, socioeconomic status, familial connections, personality traits, values, preferences, health, and well-being. To amplify research opportunities further, we have extended the SOEP Innovation Sample (SOEP-IS) by collecting genetic data from 2,598 participants, yielding the first genotyped dataset for Germany based on a representative population sample (SOEP-G). The sample includes 107 full-sibling pairs, 501 parent-offspring pairs, and 152 triads, which overlap with the parent-offspring pairs. Leveraging the results from well-powered genome-wide association studies, we created a repository comprising 66 polygenic indices (PGIs) in the SOEP-G sample. We show that the PGIs for height, BMI, and educational attainment capture 22∼24%, 12∼13%, and 9% of the variance in the respective phenotypes. Using the PGIs for height and BMI, we demonstrate that the considerable increase in average height and the decrease in average BMI in more recent birth cohorts cannot be attributed to genetic shifts within the German population or to age effects alone. These findings suggest an important role of improved environmental conditions in driving these changes. Furthermore, we show that higher values in the PGIs for educational attainment and the highest math class are associated with better self-rated health, illustrating complex relationships between genetics, cognition, behavior, socio-economic status, and health. In summary, the SOEP-G data and the PGI repository we created provide a valuable resource for studying individual differences, inequalities, life-course development, health, and interactions between genetic predispositions and the environment.

Self-Assembled Nanocoatings Protect Microbial Fertilizers for Climate-Resilient Agriculture

Chemical fertilizers have been crucial for sustaining the current global population by supplementing overused farmland to support consistent food production, but their use is unsustainable. Pseudomonas chlororaphis is a nitrogen-fixing bacterium that could be used as a fertilizer replacement, but this microbe is delicate. It is sensitive to stressors, such as freeze-drying and high temperatures. Here, we demonstrate protection of P. chlororaphis from freeze-drying, high temperatures (50 oC), and high humidity using self-assembling metal-phenolic network (MPN) coatings. The composition of the MPN is found to significantly impact its protective efficacy, and with optimized compositions, no viability loss is observed for MPN-coated microbes under conditions where uncoated cells do not survive. Further, we demonstrate that MPN-coated microbes improve germination of seeds by 150% as compared to those treated with fresh P. chlororaphis. Taken together, these results demonstrate the protective capabilities of MPNs against environmental stressors and represent a critical step towards enabling the production and storage of delicate microbes under nonideal conditions.

Immune activation state modulates infant engram expression across development

Infantile amnesia is possibly the most ubiquitous form of memory loss in mammals. We investigated how memories are stored in the brain throughout development by integrating engram labeling technology with mouse models of infantile amnesia. Here, we found a phenomenon in which male offspring in maternal immune activation models of autism spectrum disorder do not experience infantile amnesia. Maternal immune activation altered engram ensemble size and dendritic spine plasticity. We rescued the same apparently forgotten infantile memories in neurotypical mice by optogenetically reactivating dentate gyrus engram cells labeled during complex experiences in infancy. Furthermore, we permanently reinstated lost infantile memories by artificially updating the memory engram, demonstrating that infantile amnesia is a reversible process. Our findings suggest not only that infantile amnesia is due to a reversible retrieval deficit in engram expression but also that immune activation during development modulates innate, and reversible, forgetting switches that determine whether infantile amnesia will occur.

Conscious processing of global and local auditory irregularities causes differentiated heartbeat-evoked responses

Recent research suggests that brain-heart interactions are associated with perceptual and self-consciousness. In this line, the neural responses to visceral inputs have been hypothesized to play a leading role in shaping our subjective experience. This study aims to investigate whether the contextual processing of auditory irregularities modulates both direct neuronal responses to the auditory stimuli (ERPs) and the neural responses to heartbeats, as measured with heartbeat-evoked responses (HERs). HERs were computed in patients with disorders of consciousness, diagnosed with a minimally conscious state or unresponsive wakefulness syndrome. We tested whether HERs reflect conscious auditory perception, which can potentially provide additional information for the consciousness diagnosis. EEG recordings were taken during the local-global paradigm, which evaluates the capacity of a patient to detect the appearance of auditory irregularities at local (short-term) and global (long-term) levels. The results show that local and global effects produce distinct ERPs and HERs, which can help distinguish between the minimally conscious state and unresponsive wakefulness syndrome patients. Furthermore, we found that ERP and HER responses were not correlated suggesting that independent neuronal mechanisms are behind them. These findings suggest that HER modulations in response to auditory irregularities, especially local irregularities, may be used as a novel neural marker of consciousness and may aid in the bedside diagnosis of disorders of consciousness with a more cost-effective option than neuroimaging methods.

Complex Three-Dimensional Microscale Structures for Quantum Sensing Applications

We present a novel method for fabricating highly customizable three-dimensional structures hosting quantum sensors based on nitrogen vacancy (NV) centers using two-photon polymerization. This approach overcomes challenges associated with structuring traditional single-crystal quantum sensing platforms and enables the creation of complex, fully three-dimensional, sensor assemblies with submicroscale resolutions (down to 400 nm) and large fields of view (>1 mm). By embedding NV center-containing nanoparticles in exemplary structures, we demonstrate high sensitivity optical sensing of temperature and magnetic fields at the microscale. Our work showcases the potential for integrating quantum sensors with advanced manufacturing techniques, facilitating the incorporation of sensors into existing microfluidic and electronic platforms, and opening new avenues for widespread utilization of quantum sensors in various applications.

Elucidating the Mechanism of Large Phosphate Molecule Intercalation Through Graphene-Substrate Heterointerfaces

Intercalation is the process of inserting chemical species into the heterointerfaces of two-dimensional (2D) layered materials. While much research has focused on the intercalation of metals and small gas molecules into graphene, the intercalation of larger molecules through the basal plane of graphene remains challenging. In this work, we present a new mechanism for intercalating large molecules through monolayer graphene to form confined oxide materials at the graphene-substrate heterointerface. We investigate the intercalation of phosphorus pentoxide (P2O5) molecules directly from the vapor phase and confirm the formation of confined P2O5 at the graphene-substrate heterointerface using various techniques. Density functional theory (DFT) corroborates the experimental results and reveals the intercalation mechanism, whereby P2O5 dissociates into small fragments catalyzed by defects in the graphene that then permeates through lattice defects and reacts at the heterointerface to form P2O5. This process can also be used to form new confined metal phosphates (e.g., 2D InPO4). While the focus of this study is on P2O5 intercalation, the possibility of intercalation from predissociated molecules catalyzed by defects in graphene may exist for other types of molecules as well. This in-depth study advances our understanding of intercalation routes of large molecules via the basal plane of graphene as well as heterointerface chemical reactions leading to the formation of distinctive confined complex oxide compounds.

A longitudinal multi-site evaluation of community-based partnerships: implications for researchers, funders, and communities

BACKGROUND

Innovative Models Promoting Access to Care Transformation (IMPACT) was a five-year (2013-2018), Canadian-Australian research program that aimed to use a community-based partnership approach to transform primary health care (PHC) organizational structures to improve access to appropriate care for vulnerable populations. Local Innovation Partnerships (LIPs) were developed to support the IMPACT research program, and to be ongoing structures that would continue to drive local improvements to PHC.

METHODS

A longitudinal development-focused evaluation explored the overall approach to governance, relationships and processes of the LIPs in the IMPACT program. Semi-structured interviews were conducted with purposively selected participants including researchers with implementation roles and non-researchers who were members of LIPs at four time points: early in the development of the LIPs in 2014; during intervention development in 2015/2016; at the intervention implementation phase in 2017; and nearing completion of the research program in 2018.  A hybrid deductive-inductive thematic analysis approach was used. A Guide developed to support the program was used as the framework for designing questions and analysing data using a qualitative descriptive method initially. A visual representation was developed and refined after each round of data collection to illustrate emerging themes around governance, processes and relationship building that were demonstrated by IMPACT LIPs. After all rounds of data collection, an overarching cross-case analysis of narrative summaries of each site was conducted.

RESULTS

Common components of the LIPs identified across all rounds of data collection related to governance structures, stakeholder relationships, collaborative processes, and contextual barriers.  LIPs were seen primarily as a structure to support implementation of a research project rather than an ongoing multisectoral community-based partnership.  LIPs had relationships with many and varied stakeholders although not necessarily in ways that reflected the intended purpose. Collaboration was valued, but multiple barriers impeded the ability of LIPs to enact real collaboration in daily operations over time. We learned that experience, history, and time matter, especially with respect to community-oriented collaborative skills, structures, and relationships.

CONCLUSIONS

This longitudinal multiple case study offers lessons and implications for researchers, funders, and potential stakeholders in community-based participatory research.

Near Room-Temperature Intrinsic Exchange Bias in an Fe Intercalated ZrSe2 Spin Glass

Some magnetic systems display a shift in the center of their magnetic hysteresis loop away from zero field, a phenomenon termed exchange bias. Despite the extensive use of the exchange bias effect, particularly in magnetic multilayers, for the design of spin-based memory/electronics devices, a comprehensive mechanistic understanding of this effect remains a longstanding problem. Recent work has shown that disorder-induced spin frustration might play a key role in exchange bias, suggesting new materials design approaches for spin-based electronic devices that harness this effect. Here, we design a spin glass with strong spin frustration induced by magnetic disorder by exploiting the distinctive structure of Fe intercalated ZrSe2, where Fe(II) centers are shown to occupy both octahedral and tetrahedral interstitial sites and to distribute between ZrSe2 layers without long-range structural order. Notably, we observe behavior consistent with a magnetically frustrated and multidegenerate ground state in these Fe0.17ZrSe2 single crystals, which persists above room temperature. Moreover, this magnetic frustration leads to a robust and tunable exchange bias up to 250 K. These results not only offer important insights into the effects of magnetic disorder and frustration in magnetic materials generally, but also highlight as design strategy the idea that a large exchange bias can arise from an inhomogeneous microscopic environment without discernible long-range magnetic order. In addition, these results show that intercalated TMDs like Fe0.17ZrSe2 hold potential for spintronic technologies that can achieve room temperature applications.

Comparative Electronic Structures of the Chiral Helimagnets Cr1/3NbS2 and Cr1/3TaS2

Magnetic materials with noncollinear spin textures are promising for spintronic applications. To realize practical devices, control over the length and energy scales of such spin textures is imperative. The chiral helimagnets Cr1/3NbS2 and Cr1/3TaS2 exhibit analogous magnetic-phase diagrams with different real-space periodicities and field dependence, positioning them as model systems for studying the relative strengths of the microscopic mechanisms giving rise to exotic spin textures. Although the electronic structure of the Nb analogue has been experimentally investigated, the Ta analogue has received far less attention. Here, we present a comprehensive suite of electronic structure studies on both Cr1/3NbS2 and Cr1/3TaS2 using angle-resolved photoemission spectroscopy and density functional theory. We show that bands in Cr1/3TaS2 are more dispersive than their counterparts in Cr1/3NbS2, resulting in markedly different Fermi wavevectors. The fact that their qualitative magnetic phase diagrams are nevertheless identical shows that hybridization between the intercalant and host lattice mediates the magnetic exchange interactions in both of these materials. We ultimately find that ferromagnetic coupling is stronger in Cr1/3TaS2, but larger spin-orbit coupling (and a stronger Dzyaloshinskii-Moriya interaction) from the heavier host lattice ultimately gives rise to shorter spin textures.

Applying an evolutionary mismatch framework to understand disease susceptibility

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Inter-household transfers of material goods among Sama "sea nomads" of the Philippines: Reciprocity, helping, signaling, or something else?

The extent to which humans share with both kin and non-kin is a defining characteristic of our species. Evolutionary research suggests that pervasive reliance on inter-individual transfers of goods and services may have evolved to support a cooperative breeding adaptation in humans. However, while intensive food sharing between individuals and families has frequently been investigated in small-scale human societies, a comprehensive analysis of the daily transfers of all material goods has not been attempted. Likewise, while much previous research on cooperative transfers focused on terrestrial foraging populations, less attention is paid to other small-scale economic modalities traditionally inhabited by humans. Drawing on over three years' worth of interviews and observational data from a community of primarily ethnic Sama people residing along the coast of Southern Mindanao Island in the Philippines, this paper examines the overall transfer patterns of material goods in a marine foraging economy. A quantitative description of resource acquisition is followed by an in-depth exploration of the characteristics of individual households and household dyads who gave and/or received more during the study period. Results indicate that a household's age and income are consistently correlated with increased inflow and outflow of material goods. Results also suggest differential motivations underlie inter-household sharing of food, money, and other goods in the study community. Most importantly, we find that both daily and long-term reciprocity overwhelmingly drive sharing within household dyads in the study community, despite secondary effects of kinship, relative need, and relative household age between household dyads.

Babies, bugs and brains: How the early microbiome associates with infant brain and behavior development

Growing evidence is demonstrating the connection between the microbiota gut-brain axis and neurodevelopment. Microbiota colonization occurs before the maturation of many neural systems and is linked to brain health. Because of this it has been hypothesized that the early microbiome interactions along the gut-brain axis evolved to promote advanced cognitive functions and behaviors. Here, we performed a pilot study with a multidisciplinary approach to test if the microbiota composition of infants is associated with measures of early cognitive development, in particular neural rhythm tracking; language (forward speech) versus non-language (backwards speech) discrimination; and social joint attention. Fecal samples were collected from 56 infants between four and six months of age and sequenced by shotgun metagenomic sequencing. Of these, 44 performed the behavioral Point and Gaze test to measure joint attention. Infants were tested on either language discrimination using functional near-infrared spectroscopy (fNIRS; 25 infants had usable data) or neural rhythm tracking using electroencephalogram (EEG; 15 had usable data). Infants who succeeded at the Point and Gaze test tended to have increased Actinobacteria and reduced Firmicutes at the phylum level; and an increase in Bifidobacterium and Eggerthella along with a reduction in Hungatella and Streptococcus at the genus level. Measurements of neural rhythm tracking associated negatively to the abundance of Bifidobacterium and positively to the abundance of Clostridium and Enterococcus for the bacterial abundances, and associated positively to metabolic pathways that can influence neurodevelopment, including branched chain amino acid biosynthesis and pentose phosphate pathways. No associations were found for the fNIRS language discrimination measurements. Although the tests were underpowered due to the small pilot sample sizes, potential associations were identified between the microbiome and measurements of early cognitive development that are worth exploring further.

Hybrid predictive coding: Inferring, fast and slow

Predictive coding is an influential model of cortical neural activity. It proposes that perceptual beliefs are furnished by sequentially minimising "prediction errors"-the differences between predicted and observed data. Implicit in this proposal is the idea that successful perception requires multiple cycles of neural activity. This is at odds with evidence that several aspects of visual perception-including complex forms of object recognition-arise from an initial "feedforward sweep" that occurs on fast timescales which preclude substantial recurrent activity. Here, we propose that the feedforward sweep can be understood as performing amortized inference (applying a learned function that maps directly from data to beliefs) and recurrent processing can be understood as performing iterative inference (sequentially updating neural activity in order to improve the accuracy of beliefs). We propose a hybrid predictive coding network that combines both iterative and amortized inference in a principled manner by describing both in terms of a dual optimization of a single objective function. We show that the resulting scheme can be implemented in a biologically plausible neural architecture that approximates Bayesian inference utilising local Hebbian update rules. We demonstrate that our hybrid predictive coding model combines the benefits of both amortized and iterative inference-obtaining rapid and computationally cheap perceptual inference for familiar data while maintaining the context-sensitivity, precision, and sample efficiency of iterative inference schemes. Moreover, we show how our model is inherently sensitive to its uncertainty and adaptively balances iterative and amortized inference to obtain accurate beliefs using minimum computational expense. Hybrid predictive coding offers a new perspective on the functional relevance of the feedforward and recurrent activity observed during visual perception and offers novel insights into distinct aspects of visual phenomenology.

A 14th century CE Brucella melitensis genome and the recent expansion of the Western Mediterranean clade

Brucellosis is a disease caused by the bacterium Brucella and typically transmitted through contact with infected ruminants. It is one of the most common chronic zoonotic diseases and of particular interest to public health agencies. Despite its well-known transmission history and characteristic symptoms, we lack a more complete understanding of the evolutionary history of its best-known species-Brucella melitensis. To address this knowledge gap we fortuitously found, sequenced and assembled a high-quality ancient B. melitensis draft genome from the kidney stone of a 14th-century Italian friar. The ancient strain contained fewer core genes than modern B. melitensis isolates, carried a complete complement of virulence genes, and did not contain any indication of significant antimicrobial resistances. The ancient B. melitensis genome fell as a basal sister lineage to a subgroup of B. melitensis strains within the Western Mediterranean phylogenetic group, with a short branch length indicative of its earlier sampling time, along with a similar gene content. By calibrating the molecular clock we suggest that the speciation event between B. melitensis and B. abortus is contemporaneous with the estimated time frame for the domestication of both sheep and goats. These results confirm the existence of the Western Mediterranean clade as a separate group in the 14th CE and suggest that its divergence was due to human and ruminant co-migration.

Anomalous Interfacial Electron-Transfer Kinetics in Twisted Trilayer Graphene Caused by Layer-Specific Localization

Interfacial electron-transfer (ET) reactions underpin the interconversion of electrical and chemical energy. It is known that the electronic state of electrodes strongly influences ET rates because of differences in the electronic density of states (DOS) across metals, semimetals, and semiconductors. Here, by controlling interlayer twists in well-defined trilayer graphene moirés, we show that ET rates are strikingly dependent on electronic localization in each atomic layer and not the overall DOS. The large degree of tunability inherent to moiré electrodes leads to local ET kinetics that range over 3 orders of magnitude across different constructions of only three atomic layers, even exceeding rates at bulk metals. Our results demonstrate that beyond the ensemble DOS, electronic localization is critical in facilitating interfacial ET, with implications for understanding the origin of high interfacial reactivity typically exhibited by defects at electrode-electrolyte interfaces.

Decoupling Effects of Electrostatic Gating on Electronic Transport and Interfacial Charge-Transfer Kinetics at Few-Layer Molybdenum Disulfide

The electronic properties of electrode materials play a crucial role in defining their electrochemical behavior in energy conversion and storage devices. The assembly of van der Waals heterostructures and fabrication into mesoscopic devices enable the dependence of an electrochemical response on electronic properties to be systematically interrogated. Here, we evaluate the effect of charge carrier concentration on heterogeneous electron transfer at few-layer MoS₂ electrodes by combining spatially resolved electrochemical measurements with field-effect electrostatic manipulation of band alignment. Steady-state cyclic voltammograms and finite-element simulations reveal a strong modulation of the measured electrochemical response for outer-sphere charge transfer at the electrostatic gate voltage. In addition, spatially resolved voltammetric responses, obtained at a series of locations at the surface of few-layer MoS₂, reveal the governing role of in-plane charge transport on the electrochemical behavior of 2D electrodes, especially under conditions of low carrier densities.

Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants

Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.

In vivo mapping of pharmacologically induced functional reorganization onto the human brain's neurotransmitter landscape

To understand how pharmacological interventions can exert their powerful effects on brain function, we need to understand how they engage the brain's rich neurotransmitter landscape. Here, we bridge microscale molecular chemoarchitecture and pharmacologically induced macroscale functional reorganization, by relating the regional distribution of 19 neurotransmitter receptors and transporters obtained from positron emission tomography, and the regional changes in functional magnetic resonance imaging connectivity induced by 10 different mind-altering drugs: propofol, sevoflurane, ketamine, lysergic acid diethylamide (LSD), psilocybin, N,N-Dimethyltryptamine (DMT), ayahuasca, 3,4-methylenedioxymethamphetamine (MDMA), modafinil, and methylphenidate. Our results reveal a many-to-many mapping between psychoactive drugs' effects on brain function and multiple neurotransmitter systems. The effects of both anesthetics and psychedelics on brain function are organized along hierarchical gradients of brain structure and function. Last, we show that regional co-susceptibility to pharmacological interventions recapitulates co-susceptibility to disorder-induced structural alterations. Collectively, these results highlight rich statistical patterns relating molecular chemoarchitecture and drug-induced reorganization of the brain's functional architecture.

What's a good prediction? Challenges in evaluating an agent's knowledge

Constructing general knowledge by learning task-independent models of the world can help agents solve challenging problems. However, both constructing and evaluating such models remain an open challenge. The most common approaches to evaluating models is to assess their accuracy with respect to observable values. However, the prevailing reliance on estimator accuracy as a proxy for the usefulness of the knowledge has the potential to lead us astray. We demonstrate the conflict between accuracy and usefulness through a series of illustrative examples including both a thought experiment and an empirical example in Minecraft, using the General Value Function framework (GVF). Having identified challenges in assessing an agent's knowledge, we propose an alternate evaluation approach that arises naturally in the online continual learning setting: we recommend evaluation by examining internal learning processes, specifically the relevance of a GVF's features to the prediction task at hand. This paper contributes a first look into evaluation of predictions through their use, an integral component of predictive knowledge which is as of yet unexplored.

Intracranial Entrainment Reveals Statistical Learning across Levels of Abstraction

We encounter the same people, places, and objects in predictable sequences and configurations. Humans efficiently learn these regularities via statistical learning. Importantly, statistical learning creates knowledge not only of specific regularities but also of regularities that apply more generally across related experiences (i.e., across members of a category). Prior evidence for different levels of learning comes from post-exposure behavioral tests, leaving open the question of whether more abstract regularities are detected online during initial exposure. We address this question by measuring neural entrainment in intracranial recordings. Neurosurgical patients viewed a stream of photographs with regularities at 1 of 2 levels: In the exemplar-level structured condition, the same photographs appeared repeatedly in pairs. In the category-level structured condition, the photographs were trial-unique but their categories were paired across repetitions. In a baseline random condition, the same photographs repeated but in a scrambled order. We measured entrainment at the frequency of individual photographs, which was expected in all conditions, but critically also at half that frequency-the rate at which to-be-learned pairs appeared in the 2 structured (but not random) conditions. Entrainment to both exemplar and category pairs emerged within minutes throughout visual cortex and in frontal and temporal regions. Many electrode contacts were sensitive to only one level of structure, but a significant number encoded both levels. These findings suggest that the brain spontaneously uncovers category-level regularities during statistical learning, providing insight into the brain's unsupervised mechanisms for building flexible and robust knowledge that generalizes across input variation and conceptual hierarchies.

Imidazopyridine Amides: Synthesis, Mycobacterium smegmatis CIII2CIV2 Supercomplex Binding, and In Vitro Antimycobacterial Activity

Q203 (telacebec) is an imidazopyridine amide (IPA) targeting the respiratory CIII₂CIV₂ supercomplex of the mycobacterial electron transport chain (ETC). Aiming for a better understanding of the molecular mechanism of action of IPA, 27 analogues were prepared through a seven-step synthetic scheme. Oxygen consumption assay was designed to test the inhibition of purified Mycobacterium smegmatis CIII₂CIV₂ by these compounds. The assay results generally supported structure-activity relationship information obtained from the structure of M. smegmatis CIII₂CIV₂ bound to Q203. The IC₅₀ of Q203 and compound 27 was 99 ± 32 and 441 ± 138 nM, respectively. All IPAs including Q203 showed no inhibition of mitochondrial ETC, proving their selectivity against mycobacteria. In vitro Mycobacterium tuberculosis growth inhibition and M. smegmatis CIII₂CIV₂ binding did not correlate perfectly. These observations suggest that further investigation into the mechanisms of resistance in different mycobacterial species is needed to understand the lack of the correlation pattern between CIII₂CIV₂ inhibition and cellular activity.

Bridging Structure, Magnetism, and Disorder in Iron-Intercalated Niobium Diselenide, FexNbSe2, below x = 0.25

Transition-metal dichalcogenides (TMDs) intercalated with magnetic ions serve as a promising materials platform for developing next-generation, spin-based electronic technologies. In these materials, one can access a rich magnetic phase space depending on the choice of intercalant, host lattice, and relative stoichiometry. The distribution of these intercalant ions across given crystals, however, is less well defined-particularly away from ideal packing stoichiometries-and a convenient probe to assess potential longer-range ordering of intercalants is lacking. Here, we demonstrate that confocal Raman spectroscopy is a powerful tool for mapping the onset of intercalant superlattice formation in Fe-intercalated NbSe₂ (Fe_xNbSe₂) for 0.14 ≤ x < 0.25. We use single-crystal X-ray diffraction to confirm the presence of longer-range intercalant superstructure and employ polarization-, temperature-, and magnetic field-dependent Raman measurements to examine both the symmetry of emergent phonon modes in the intercalated material and potential magnetoelastic coupling. Magnetometry measurements further indicate a correlation between the onset of magnetic ordering and the relative degree of intercalant superlattice formation. These results show Raman spectroscopy to be an expedient, local probe for mapping intercalant ordering in this class of magnetic materials.

Identification of the Neoaspergillic Acid Biosynthesis Gene Cluster by Establishing an In Vitro CRISPR-Ribonucleoprotein Genetic System in Aspergillus melleus

Filamentous fungi are an essential source of bioactive mycotoxins. Recent efforts have focused on developing antifungal agents that are effective against invasive yeasts, such as Candida spp. By screening fungal strains isolated from regions surrounding the Chernobyl nuclear power plant disaster for antifungal activity against Candida albicans, we found that Aspergillus melleus IMV 01140 produced compounds that inhibited the growth of the yeast. The active compound produced by A. melleus was isolated and found to be neoaspergillic acid, a compound that is closely related to aspergillic acid. While aspergillic acid and its derivatives have been characterized and were found to have antibacterial and antifungal properties, neoaspergillic acid has been much less studied. Even though neoaspergillic acid and related compounds were found to have antibacterial and antitumoral effects, further investigation into this group of compounds is limited by challenges associated with large-scale production, isolation, and purification. The production of neoaspergillic acid has been shown to require co-cultivation methods or special growth conditions. In this work, neoaspergillic acid and related compounds were found to be produced by A. melleus under laboratory growth conditions. The biosynthetic gene cluster of neoaspergillic acid was predicted using the aspergillic acid gene cluster as a model. The biosynthetic pathway for neoaspergillic acid was then confirmed by establishing an in vitro CRISPR-ribonucleoprotein system to individually delete genes within the cluster. A negative transcriptional factor, mcrA, was also eliminated to further improve the production of neoaspergillic acid and the related compounds for future studies.

Judging facts, judging norms: Training machine learning models to judge humans requires a modified approach to labeling data

As governments and industry turn to increased use of automated decision systems, it becomes essential to consider how closely such systems can reproduce human judgment. We identify a core potential failure, finding that annotators label objects differently depending on whether they are being asked a factual question or a normative question. This challenges a natural assumption maintained in many standard machine-learning (ML) data acquisition procedures: that there is no difference between predicting the factual classification of an object and an exercise of judgment about whether an object violates a rule premised on those facts. We find that using factual labels to train models intended for normative judgments introduces a notable measurement error. We show that models trained using factual labels yield significantly different judgments than those trained using normative labels and that the impact of this effect on model performance can exceed that of other factors (e.g., dataset size) that routinely attract attention from ML researchers and practitioners.

Viral predation pressure on coral reefs

BACKGROUND

Predation pressure and herbivory exert cascading effects on coral reef health and stability. However, the extent of these cascading effects can vary considerably across space and time. This variability is likely a result of the complex interactions between coral reefs' biotic and abiotic dimensions. A major biological component that has been poorly integrated into the reefs' trophic studies is the microbial community, despite its role in coral death and bleaching susceptibility. Viruses that infect bacteria can control microbial densities and may positively affect coral health by controlling microbialization. We hypothesize that viral predation of bacteria has analogous effects to the top-down pressure of macroorganisms on the trophic structure and reef health.

RESULTS

Here, we investigated the relationships between live coral cover and viruses, bacteria, benthic algae, fish biomass, and water chemistry in 110 reefs spanning inhabited and uninhabited islands and atolls across the Pacific Ocean. Statistical learning showed that the abundance of turf algae, viruses, and bacteria, in that order, were the variables best predicting the variance in coral cover. While fish biomass was not a strong predictor of coral cover, the relationship between fish and corals became apparent when analyzed in the context of viral predation: high coral cover (> 50%) occurred on reefs with a combination of high predator fish biomass (sum of sharks and piscivores > 200 g m^-2) and high virus-to-bacteria ratios (> 10), an indicator of viral predation pressure. However, these relationships were non-linear, with reefs at the higher and lower ends of the coral cover continuum displaying a narrow combination of abiotic and biotic variables, while reefs at intermediate coral cover showed a wider range of parameter combinations.

CONCLUSIONS

The results presented here support the hypothesis that viral predation of bacteria is associated with high coral cover and, thus, coral health and stability. We propose that combined predation pressures from fishes and viruses control energy fluxes, inhibiting the detrimental accumulation of ecosystem energy in the microbial food web.

Evaluating the abiotic synthesis potential and the stability of building blocks of life beneath an impact-induced steam atmosphere

A prerequisite for prebiotic chemistry is the accumulation of critical building blocks of life. Some studies argue that more frequent impact events on the primitive Earth could have induced a more reducing steam atmosphere and thus favor widespread and more efficient synthesis of life building blocks. However, elevated temperature is also proposed to threaten the stability of organics and whether life building blocks could accumulate to appreciable levels in the reducing yet hot surface seawater beneath the steam atmosphere is still poorly examined. Here, we used a thermodynamic tool to examine the synthesis affinity of various life building blocks using inorganic gasses as reactants at elevated temperatures and corresponding steam pressures relevant with the steam-seawater interface. Our calculations show that although the synthesis affinity of all life building blocks decreases when temperature increases, many organics, including methane, methanol, and carboxylic acids, have positive synthesis affinity over a wide range of temperatures, implying that these species were favorable to form (>10-6 molal) in the surface seawater. However, cyanide and formaldehyde have overall negative affinities, suggesting that these critical compounds would tend to undergo hydrolysis in the surface seawaters. Most of the 18 investigated amino acids have positive affinities at temperature <220°C and their synthesis affinity increases under more alkaline conditions. Sugars, ribose, and nucleobases have overall negative synthesis affinities at the investigated range of temperatures. Synthesis affinities are shown to be sensitive to the hydrogen fugacity. Higher hydrogen fugacity (in equilibrium with FQI or IW) favors the synthesis and accumulation of nearly all the investigated compounds, except for HCN and its derivate products. In summary, our results suggest that reducing conditions induced by primitive impacts could indeed favor the synthesis/accumulation of some life building blocks, but some critical species, particularly HCN and nucleosides, were still unfavorable to accumulate to appreciable levels. Our results can provide helpful guidance for future efforts to search for or understand the stability of biomolecules on other planets like Mars and icy moons. We advocate examining craters formed by more reducing impactors to look for the preservation of prebiotic materials.

Gut commensal Enterocloster species host inoviruses that are secreted in vitro and in vivo

BACKGROUND

Bacteriophages in the family Inoviridae, or inoviruses, are under-characterized phages previously implicated in bacterial pathogenesis by contributing to biofilm formation, immune evasion, and toxin secretion. Unlike most bacteriophages, inoviruses do not lyse their host cells to release new progeny virions; rather, they encode a secretion system that actively pumps them out of the bacterial cell. To date, no inovirus associated with the human gut microbiome has been isolated or characterized.

RESULTS

In this study, we utilized in silico, in vitro, and in vivo methods to detect inoviruses in bacterial members of the gut microbiota. By screening a representative genome library of gut commensals, we detected inovirus prophages in Enterocloster spp. (formerly Clostridium spp.). We confirmed the secretion of inovirus particles in in vitro cultures of these organisms using imaging and qPCR. To assess how the gut abiotic environment, bacterial physiology, and inovirus secretion may be linked, we deployed a tripartite in vitro assay that progressively evaluated bacterial growth dynamics, biofilm formation, and inovirus secretion in the presence of changing osmotic environments. Counter to other inovirus-producing bacteria, inovirus production was not correlated with biofilm formation in Enterocloster spp. Instead, the Enterocloster strains had heterogeneous responses to changing osmolality levels relevant to gut physiology. Notably, increasing osmolality induced inovirus secretion in a strain-dependent manner. We confirmed inovirus secretion in a gnotobiotic mouse model inoculated with individual Enterocloster strains in vivo in unperturbed conditions. Furthermore, consistent with our in vitro observations, inovirus secretion was regulated by a changed osmotic environment in the gut due to osmotic laxatives.

CONCLUSION

In this study, we report on the detection and characterization of novel inoviruses from gut commensals in the Enterocloster genus. Together, our results demonstrate that human gut-associated bacteria can secrete inoviruses and begin to elucidate the environmental niche filled by inoviruses in commensal bacteria. Video Abstract.

Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity

BACKGROUND

Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors.

OBJECTIVE

Here, we bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants.

METHODS

In both brain assessments, we used penalized logistic regression models and nonparametric permutation.

RESULTS

We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings.

CONCLUSIONS

These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.

From eye-blinks to state construction: Diagnostic benchmarks for online representation learning

We present three new diagnostic prediction problems inspired by classical-conditioning experiments to facilitate research in online prediction learning. Experiments in classical conditioning show that animals such as rabbits, pigeons, and dogs can make long temporal associations that enable multi-step prediction. To replicate this remarkable ability, an agent must construct an internal state representation that summarizes its interaction history. Recurrent neural networks can automatically construct state and learn temporal associations. However, the current training methods are prohibitively expensive for online prediction-continual learning on every time step-which is the focus of this paper. Our proposed problems test the learning capabilities that animals readily exhibit and highlight the limitations of the current recurrent learning methods. While the proposed problems are nontrivial, they are still amenable to extensive testing and analysis in the small-compute regime, thereby enabling researchers to study issues in isolation, ultimately accelerating progress towards scalable online representation learning methods.

Activity-Based Sensing for Chemistry-Enabled Biology: Illuminating Principles, Probes, and Prospects for Boronate Reagents for Studying Hydrogen Peroxide

Activity-based sensing (ABS) offers a general approach that exploits chemical reactivity as a method for selective detection and manipulation of biological analytes. Here, we illustrate the value of this chemical platform to enable new biological discovery through a case study in the design and application of ABS reagents for studying hydrogen peroxide (H2O2), a major type of reactive oxygen species (ROS) that regulates a diverse array of vital cellular signaling processes to sustain life. Specifically, we summarize advances in the use of activity-based boronate probes for the detection of H2O2 featuring high molecular selectivity over other ROS, with an emphasis on tailoring designs in chemical structure to promote new biological principles of redox signaling.