Autonomous synthesis and characterization of inorganic materials require the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning ...algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. Larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.
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IJS, KILJ, NUK, PNG, UL, UM
Tautomeric and anionic Watson-Crick-like mismatches have important roles in replication and translation errors through mechanisms that are not fully understood. Here, using NMR relaxation dispersion, ...we resolve a sequence-dependent kinetic network connecting G•T/U wobbles with three distinct Watson-Crick mismatches: two rapidly exchanging tautomeric species (G
•T/UG•T
/U
; population less than 0.4%) and one anionic species (G•T
/U
; population around 0.001% at neutral pH). The sequence-dependent tautomerization or ionization step was inserted into a minimal kinetic mechanism for correct incorporation during replication after the initial binding of the nucleotide, leading to accurate predictions of the probability of dG•dT misincorporation across different polymerases and pH conditions and for a chemically modified nucleotide, and providing mechanisms for sequence-dependent misincorporation. Our results indicate that the energetic penalty for tautomerization and/or ionization accounts for an approximately 10
to 10
-fold discrimination against misincorporation, which proceeds primarily via tautomeric dG
•dT and dG•dT
, with contributions from anionic dG•dT
dominant at pH 8.4 and above or for some mutagenic nucleotides.
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The field of photopharmacology aims to introduce smart drugs that, through the incorporation of molecular photoswitches, allow for the remote spatial and temporal control of bioactivity by light. ...This concept could be particularly beneficial in the treatment of bacterial infections, by reducing the systemic and environmental side effects of antibiotics. A major concern in the realization of such light-responsive drugs is the wavelength of the light that is applied. Studies on the photocontrol of biologically active agents mostly rely on UV light, which is cytotoxic and poorly suited for tissue penetration. In our efforts to develop photoswitchable antibiotics, we introduce here antibacterial agents whose activity can be controlled by visible light, while getting into the therapeutic window. For that purpose, a UV-light-responsive core structure based on diaminopyrimidines with suitable antibacterial properties was identified. Subsequent modification of an azobenzene photoswitch moiety led to structures that allowed us to control their activity against Escherichia coli in both directions with light in the visible region. For the first time, full in situ photocontrol of antibacterial activity in the presence of bacteria was attained with green and violet light. Most remarkably, one of the diaminopyrimidines revealed an at least 8-fold difference in activity before and after irradiation with red light at 652 nm, showcasing the effective “activation” of a biological agent otherwise inactive within the investigated concentration range, and doing so with red light in the therapeutic window.
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Abstract
Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding ...of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS
3
) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material’s formation. Based on this information, ARROWS
3
proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS
3
identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in ...the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.
We review recent progress in the development of self-driving laboratories and discuss their application to autonomous inorganic materials synthesis.
Mapping neural circuits across defined synapses is essential for understanding brain function. Here we describe trans-Tango, a technique for anterograde transsynaptic circuit tracing and ...manipulation. At the core of trans-Tango is a synthetic signaling pathway that is introduced into all neurons in the animal. This pathway converts receptor activation at the cell surface into reporter expression through site-specific proteolysis. Specific labeling is achieved by presenting a tethered ligand at the synapses of genetically defined neurons, thereby activating the pathway in their postsynaptic partners and providing genetic access to these neurons. We first validated trans-Tango in the Drosophila olfactory system and then implemented it in the gustatory system, where projections beyond the first-order receptor neurons are not fully characterized. We identified putative second-order neurons within the sweet circuit that include projection neurons targeting known neuromodulation centers in the brain. These experiments establish trans-Tango as a flexible platform for transsynaptic circuit analysis.
•A genetic approach for transsynaptic tracing and manipulation of neural circuits•Genetic access to neurons based on their connectivity•Glomerulus-specific patterns of second-order neurons in the fly olfactory system•Identifying second-order gustatory neurons and their target areas in the fly brain
Talay and Richman et al. develop a genetic method for transsynaptic labeling of neural circuits in Drosophila. They validate it in the olfactory system and implement it in the gustatory system to reveal second-order projections of sweet tastant-responsive neurons.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Objective
Most SARS‐CoV‐2‐infected individuals never require hospitalization. However, some develop prolonged symptoms. We sought to characterize the spectrum of neurologic manifestations in ...non‐hospitalized Covid‐19 “long haulers”.
Methods
This is a prospective study of the first 100 consecutive patients (50 SARS‐CoV‐2 laboratory‐positive (SARS‐CoV‐2+) and 50 laboratory‐negative (SARS‐CoV‐2‐) individuals) presenting to our Neuro‐Covid‐19 clinic between May and November 2020. Due to early pandemic testing limitations, patients were included if they met Infectious Diseases Society of America symptoms of Covid‐19, were never hospitalized for pneumonia or hypoxemia, and had neurologic symptoms lasting over 6 weeks. We recorded the frequency of neurologic symptoms and analyzed patient‐reported quality of life measures and standardized cognitive assessments.
Results
Mean age was 43.2 ± 11.3 years, 70% were female, and 48% were evaluated in televisits. The most frequent comorbidities were depression/anxiety (42%) and autoimmune disease (16%). The main neurologic manifestations were: “brain fog” (81%), headache (68%), numbness/tingling (60%), dysgeusia (59%), anosmia (55%), and myalgias (55%), with only anosmia being more frequent in SARS‐CoV‐2+ than SARS‐CoV‐2‐ patients (37/50 74% vs. 18/50 36%; p < 0.001). Moreover, 85% also experienced fatigue. There was no correlation between time from disease onset and subjective impression of recovery. Both groups exhibited impaired quality of life in cognitive and fatigue domains. SARS‐CoV‐2+ patients performed worse in attention and working memory cognitive tasks compared to a demographic‐matched US population (T‐score 41.5 37, 48.25 and 43 37.5, 48.75, respectively; both p < 0.01).
Interpretation
Non‐hospitalized Covid‐19 “long haulers” experience prominent and persistent “brain fog” and fatigue that affect their cognition and quality of life.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The rise of novelty in ecosystems Radeloff, Volker C; Williams, John W; Bateman, Brooke L ...
Ecological applications,
December 2015, Volume:
25, Issue:
8
Journal Article
Peer reviewed
Rapid and ongoing change creates novelty in ecosystems everywhere, both when comparing contemporary systems to their historical baselines, and predicted future systems to the present. However, the ...level of novelty varies greatly among places. Here we propose a formal and quantifiable definition of abiotic and biotic novelty in ecosystems, map abiotic novelty globally, and discuss the implications of novelty for the science of ecology and for biodiversity conservation. We define novelty as the degree of dissimilarity of a system, measured in one or more dimensions relative to a reference baseline, usually defined as either the present or a time window in the past. In this conceptualization, novelty varies in degree, it is multidimensional, can be measured, and requires a temporal and spatial reference. This definition moves beyond prior categorical definitions of novel ecosystems, and does not include human agency, self-perpetuation, or irreversibility as criteria. Our global assessment of novelty was based on abiotic factors (temperature, precipitation, and nitrogen deposition) plus human population, and shows that there are already large areas with high novelty today relative to the early 20th century, and that there will even be more such areas by 2050. Interestingly, the places that are most novel are often not the places where absolute changes are largest; highlighting that novelty is inherently different from change. For the ecological sciences, highly novel ecosystems present new opportunities to test ecological theories, but also challenge the predictive ability of ecological models and their validation. For biodiversity conservation, increasing novelty presents some opportunities, but largely challenges. Conservation action is necessary along the entire continuum of novelty, by redoubling efforts to protect areas where novelty is low, identifying conservation opportunities where novelty is high, developing flexible yet strong regulations and policies, and establishing long-term experiments to test management approaches. Meeting the challenge of novelty will require advances in the science of ecology, and new and creative conservation approaches.
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BFBNIB, FZAB, GIS, IJS, INZLJ, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
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