DNA-based ForceChrono probes for deciphering single-molecule force dynamics in living cells.

Understanding cellular force transmission dynamics is crucial in mechanobiology. We developed the DNA-based ForceChrono probe to measure force magnitude, duration, and loading rates at the single-molecule level within living cells. The ForceChrono probe circumvents the limitations of in vitro single-molecule force spectroscopy by enabling direct measurements within the dynamic cellular environment. Our findings reveal integrin force loading rates of 0.5-2 pN/s and durations ranging from tens of seconds in nascent adhesions to approximately 100 s in mature focal adhesions. The probe's robust and reversible design allows for continuous monitoring of these dynamic changes as cells undergo morphological transformations. Additionally, by analyzing how mutations, deletions, or pharmacological interventions affect these parameters, we can deduce the functional roles of specific proteins or domains in cellular mechanotransduction. The ForceChrono probe provides detailed insights into the dynamics of mechanical forces, advancing our understanding of cellular mechanics and the molecular mechanisms of mechanotransduction.

A selective antibiotic for Lyme disease.

Lyme disease is on the rise. Caused by a spirochete Borreliella burgdorferi, it affects an estimated 500,000 people in the United States alone. The antibiotics currently used to treat Lyme disease are broad spectrum, damage the microbiome, and select for resistance in non-target bacteria. We therefore sought to identify a compound acting selectively against B. burgdorferi. A screen of soil micro-organisms revealed a compound highly selective against spirochetes, including B. burgdorferi. Unexpectedly, this compound was determined to be hygromycin A, a known antimicrobial produced by Streptomyces hygroscopicus. Hygromycin A targets the ribosomes and is taken up by B. burgdorferi, explaining its selectivity. Hygromycin A cleared the B. burgdorferi infection in mice, including animals that ingested the compound in a bait, and was less disruptive to the fecal microbiome than clinically relevant antibiotics. This selective antibiotic holds the promise of providing a better therapeutic for Lyme disease and eradicating it in the environment.

CD4+ T cell calibration of antigen-presenting cells optimizes antiviral CD8+ T cell immunity.

Antiviral CD8+ T cell immunity depends on the integration of various contextual cues, but how antigen-presenting cells (APCs) consolidate these signals for decoding by T cells remains unclear. Here, we describe gradual interferon-α/interferon-ß (IFNα/ß)-induced transcriptional adaptations that endow APCs with the capacity to rapidly activate the transcriptional regulators p65, IRF1 and FOS after CD4+ T cell-mediated CD40 stimulation. While these responses operate through broadly used signaling components, they induce a unique set of co-stimulatory molecules and soluble mediators that cannot be elicited by IFNα/ß or CD40 alone. These responses are critical for the acquisition of antiviral CD8+ T cell effector function, and their activity in APCs from individuals infected with severe acute respiratory syndrome coronavirus 2 correlates with milder disease. These observations uncover a sequential integration process whereby APCs rely on CD4+ T cells to select the innate circuits that guide antiviral CD8+ T cell responses.

Global prediction of extreme floods in ungauged watersheds.

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.

Single-year radiocarbon dating anchors Viking Age trade cycles in time.

Recent discoveries of rapid changes in the atmospheric 14C concentration linked to solar particle events have spurred the construction of new radiocarbon annual calibration datasets1-13. With these datasets, radiocarbon dating becomes relevant for urban sites, which require dates at higher resolution than previous calibration datasets could offer. Here we use a single-year radiocarbon calibration curve to anchor the archaeological stratigraphy of a Viking Age trade centre in time. We present absolutely dated evidence for artefact finds charting the expansion of long-distance trade from as far away as Arctic Norway and the Middle East, which we linked to the beginning of the Viking Age at AD 790 ± 10. The methods developed here enable human interactions and cultural, climatic and environmental changes to be compared in archaeological stratigraphies worldwide.

Regional-specific calibration enables application of computational evidence for clinical classification of 5' cis-regulatory variants in Mendelian disease.

To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3 ≥25.3; BP4 ≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5' cis-regulatory region variants.

An Accurate and Rapidly Calibrating Speech Neuroprosthesis.

BACKGROUND: Brain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy. METHODS: A 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice. RESULTS: On the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours. CONCLUSIONS: In a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.).

Equity is more important for the social cost of methane than climate uncertainty.

The social cost of methane (SC-CH4) measures the economic loss of welfare caused by emitting one tonne of methane into the atmosphere. This valuation may in turn be used in cost-benefit analyses or to inform climate policies1-3. However, current SC-CH4 estimates have not included key scientific findings and observational constraints. Here we estimate the SC-CH4 by incorporating the recent upward revision of 25 per cent to calculations of the radiative forcing of methane4, combined with calibrated reduced-form global climate models and an ensemble of integrated assessment models (IAMs). Our multi-model mean estimate for the SC-CH4 is US$933 per tonne of CH4 (5-95 per cent range, US$471-1,570 per tonne of CH4) under a high-emissions scenario (Representative Concentration Pathway (RCP) 8.5), a 22 per cent decrease compared to estimates based on the climate uncertainty framework used by the US federal government5. Our ninety-fifth percentile estimate is 51 per cent lower than the corresponding figure from the US framework. Under a low-emissions scenario (RCP 2.6), our multi-model mean decreases to US$710 per tonne of CH4. Tightened equilibrium climate sensitivity estimates paired with the effect of previously neglected relationships between uncertain parameters of the climate model lower these estimates. We also show that our SC-CH4 estimates are sensitive to model combinations; for example, within one IAM, different methane cycle sub-models can induce variations of approximately 20 per cent in the estimated SC-CH4. But switching IAMs can more than double the estimated SC-CH4. Extending our results to account for societal concerns about equity produces SC-CH4 estimates that differ by more than an order of magnitude between low- and high-income regions. Our central equity-weighted estimate for the USA increases to US$8,290 per tonne of CH4 whereas our estimate for sub-Saharan Africa decreases to US$134 per tonne of CH4.

Non-ammocoete larvae of Palaeozoic stem lampreys.

Ammocoetes-the filter-feeding larvae of modern lampreys-have long influenced hypotheses of vertebrate ancestry1-7. The life history of modern lampreys, which develop from a superficially amphioxus-like ammocoete to a specialized predatory adult, appears to recapitulate widely accepted scenarios of vertebrate origin. However, no direct evidence has validated the evolutionary antiquity of ammocoetes, and their status as models of primitive vertebrate anatomy is uncertain. Here we report larval and juvenile forms of four stem lampreys from the Palaeozoic era (Hardistiella, Mayomyzon, Pipiscius, and Priscomyzon), including a hatchling-to-adult growth series of the genus Priscomyzon from Late Devonian Gondwana. Larvae of all four genera lack the defining traits of ammocoetes. They instead display features that are otherwise unique to adult modern lampreys, including prominent eyes, a cusped feeding apparatus, and posteriorly united branchial baskets. Notably, phylogenetic analyses find that these non-ammocoete larvae occur in at least three independent lineages of stem lamprey. This distribution strongly implies that ammocoetes are specializations of modern-lamprey life history rather than relics of vertebrate ancestry. These phylogenetic insights also suggest that the last common ancestor of hagfishes and lampreys was a macrophagous predator that did not have a filter-feeding larval phase. Thus, the armoured 'ostracoderms' that populate the cyclostome and gnathostome stems might serve as better proxies than living cyclostomes for the last common ancestor of all living vertebrates.

Drivers and dynamics of a massive adaptive radiation in cichlid fishes.

Adaptive radiation is the likely source of much of the ecological and morphological diversity of life1-4. How adaptive radiations proceed and what determines their extent remains unclear in most cases1,4. Here we report the in-depth examination of the spectacular adaptive radiation of cichlid fishes in Lake Tanganyika. On the basis of whole-genome phylogenetic analyses, multivariate morphological measurements of three ecologically relevant trait complexes (body shape, upper oral jaw morphology and lower pharyngeal jaw shape), scoring of pigmentation patterns and approximations of the ecology of nearly all of the approximately 240 cichlid species endemic to Lake Tanganyika, we show that the radiation occurred within the confines of the lake and that morphological diversification proceeded in consecutive trait-specific pulses of rapid morphospace expansion. We provide empirical support for two theoretical predictions of how adaptive radiations proceed, the 'early-burst' scenario1,5 (for body shape) and the stages model1,6,7 (for all traits investigated). Through the analysis of two genomes per species and by taking advantage of the uneven distribution of species in subclades of the radiation, we further show that species richness scales positively with per-individual heterozygosity, but is not correlated with transposable element content, number of gene duplications or genome-wide levels of selection in coding sequences.

Burden and characteristics of COVID-19 in the United States during 2020.

The COVID-19 pandemic disrupted health systems and economies throughout the world during 2020 and was particularly devastating for the United States, which experienced the highest numbers of reported cases and deaths during 20201-3. Many of the epidemiological features responsible for observed rates of morbidity and mortality have been reported4-8; however, the overall burden and characteristics of COVID-19 in the United States have not been comprehensively quantified. Here we use a data-driven model-inference approach to simulate the pandemic at county-scale in the United States during 2020 and estimate critical, time-varying epidemiological properties underpinning the dynamics of the virus. The pandemic in the United States during 2020 was characterized by national ascertainment rates that increased from 11.3% (95% credible interval (CI): 8.3-15.9%) in March to 24.5% (18.6-32.3%) during December. Population susceptibility at the end of the year was 69.0% (63.6-75.4%), indicating that about one third of the US population had been infected. Community infectious rates, the percentage of people harbouring a contagious infection, increased above 0.8% (0.6-1.0%) before the end of the year, and were as high as 2.4% in some major metropolitan areas. By contrast, the infection fatality rate fell to 0.3% by year's end.

Extant timetrees are consistent with a myriad of diversification histories.

Time-calibrated phylogenies of extant species (referred to here as 'extant timetrees') are widely used for estimating diversification dynamics1. However, there has been considerable debate surrounding the reliability of these inferences2-5 and, to date, this critical question remains unresolved. Here we clarify the precise information that can be extracted from extant timetrees under the generalized birth-death model, which underlies most existing methods of estimation. We prove that, for any diversification scenario, there exists an infinite number of alternative diversification scenarios that are equally likely to have generated any given extant timetree. These 'congruent' scenarios cannot possibly be distinguished using extant timetrees alone, even in the presence of infinite data. Importantly, congruent diversification scenarios can exhibit markedly different and yet similarly plausible dynamics, which suggests that many previous studies may have over-interpreted phylogenetic evidence. We introduce identifiable and easily interpretable variables that contain all available information about past diversification dynamics, and demonstrate that these can be estimated from extant timetrees. We suggest that measuring and modelling these identifiable variables offers a more robust way to study historical diversification dynamics. Our findings also make it clear that palaeontological data will continue to be crucial for answering some macroevolutionary questions.

Enigmatic dinosaur precursors bridge the gap to the origin of Pterosauria.

Pterosaurs were the first vertebrates to evolve powered flight1 and comprised one of the main evolutionary radiations in terrestrial ecosystems of the Mesozoic era (approximately 252-66 million years ago), but their origin has remained an unresolved enigma in palaeontology since the nineteenth century2-4. These flying reptiles have been hypothesized to be the close relatives of a wide variety of reptilian clades, including dinosaur relatives2-8, and there is still a major morphological gap between those forms and the oldest, unambiguous pterosaurs from the Upper Triassic series. Here, using recent discoveries of well-preserved cranial remains, microcomputed tomography scans of fragile skull bones (jaws, skull roofs and braincases) and reliably associated postcrania, we demonstrate that lagerpetids-a group of cursorial, non-volant dinosaur precursors-are the sister group of pterosaurs, sharing numerous synapomorphies across the entire skeleton. This finding substantially shortens the temporal and morphological gap between the oldest pterosaurs and their closest relatives and simultaneously strengthens the evidence that pterosaurs belong to the avian line of archosaurs. Neuroanatomical features related to the enhanced sensory abilities of pterosaurs9 are already present in lagerpetids, which indicates that these features evolved before flight. Our evidence illuminates the first steps of the assembly of the pterosaur body plan, whose conquest of aerial space represents a remarkable morphofunctional innovation in vertebrate evolution.

m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq.

We present m6ACali, a novel machine-learning framework aimed at enhancing the accuracy of N6-methyladenosine (m6A) epitranscriptome profiling by reducing the impact of non-specific antibody enrichment in MeRIP-Seq. The calibration model serves as a genomic feature-based classifier that refines the identification of m6A sites, distinguishing those genuinely present from those that can be detected in in-vitro transcribed (IVT) control experiments. We find that m6ACali effectively identifies non-specific binding peaks reported by exomePeak2 and MACS2 in novel MeRIP-Seq datasets without the need for paired IVT controls. The model interpretation revealed that off-target antibody binding sites commonly occur at short exons and short mRNAs, originating from high read coverage regions that share the motif sequence with true m6A sites. We also reveal that the ML strategy can efficiently adjust differentially methylated peaks and other antibody-dependent, base-resolution m6A detection techniques. As a result, m6ACali offers a promising method for the universal enhancement of m6A profiles generated by MeRIP-Seq experiments, elevating the benchmark for omics-level m6A data integration.

Calibrating Bayesian Decoders of Neural Spiking Activity.

Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.

Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria.

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

MagicalRsq: Machine-learning-based genotype imputation quality calibration.

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.

Multiple imputation of more than one environmental exposure with nondifferential measurement error.

Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R.

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

MOTIVATION: The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. RESULTS: In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. AVAILABILITY AND IMPLEMENTATION: Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.