Search for: All records

Algorithms with predictions is a new research direction that leverages machine learned predictions for algorithm design. So far a plethora of recent works have incorporated predictions to improve on worst-case bounds for online problems. In this paper, we initiate the study of complexity of dynamic data structures with predictions, including dynamic graph algorithms. Unlike online algorithms, the goal in dynamic data structures is to maintain the solution efficiently with every update. We investigate three natural models of prediction: (1) δ-accurate predictions where each predicted request matches the true request with probability δ, (2) list-accurate predictions where a true request comes from a list of possible requests, and (3) bounded delay predictions where the true requests are a permutation of the predicted requests. We give general reductions among the prediction models, showing that bounded delay is the strongest prediction model, followed by list-accurate, and δ-accurate. Further, we identify two broad problem classes based on lower bounds due to the Online Matrix Vector (OMv) conjecture. Specifically, we show that locally correctable dynamic problems have strong conditional lower bounds for list-accurate predictions that are equivalent to the non-prediction setting, unless list-accurate predictions are perfect. Moreover, we show that locally reducible dynamic problems have time complexity that degrades gracefully with the quality of bounded delay predictions. We categorize problems with known OMv lower bounds accordingly and give several upper bounds in the delay model that show that our lower bounds are almost tight. We note that concurrent work by v.d.Brand et al. [SODA '24] and Liu and Srinivas [arXiv:2307.08890] independently study dynamic graph algorithms with predictions, but their work is mostly focused on showing upper bounds. 

Ultra-faint dwarf galaxies (UFDs) are commonly found in close proximity to the Milky Way and other massive spiral galaxies. As such, their projected stellar ellipticity and extended light distributions are often thought to owe to tidal forces. In this paper, we study the projected stellar ellipticities and faint stellar outskirts of tidally isolated ultra-faints drawn from the ‘Engineering Dwarfs at Galaxy Formation’s Edge’ (EDGE) cosmological simulation suite. Despite their tidal isolation, our simulated dwarfs exhibit a wide range of projected ellipticities (0.03 < ε < 0.85), with many possessing anisotropic extended stellar haloes that mimic tidal tails, but owe instead to late-time accretion of lower mass companions. Furthermore, we find a strong causal relationship between ellipticity and formation time of a UFD, which is robust to a wide variation in the feedback model. We show that the distribution of projected ellipticities in our suite of simulated EDGE dwarfs matches well with a sample of 19 Local Group dwarf galaxies and a sample of 11 isolated dwarf galaxies. Given ellipticity in EDGE arises from an ex-situ accretion origin, the agreement in shape indicates the ellipticities of some observed dwarfs may also originate from a non-tidal scenario. The orbital parameters of these observed dwarfs further support that they are not currently tidally disrupting. If the baryonic content in these galaxies is still tidally intact, then the same may be true for their dark matter content, making these galaxies in our Local Group pristine laboratories for testing dark matter and galaxy formation models.

 

Neuronal activity propagates through the network during seizures, engaging brain dynamics at multiple scales. Such propagating events can be described through the avalanches framework, which can relate spatiotemporal activity at the microscale with global network properties. Interestingly, propagating avalanches in healthy networks are indicative of critical dynamics, where the network is organized to a phase transition, which optimizes certain computational properties. Some have hypothesized that the pathologic brain dynamics of epileptic seizures are an emergent property of microscale neuronal networks collectively driving the brain away from criticality. Demonstrating this would provide a unifying mechanism linking microscale spatiotemporal activity with emergent brain dysfunction during seizures. Here, we investigated the effect of drug-induced seizures on critical avalanche dynamics, usingin vivowhole-brain two-photon imaging of GCaMP6s larval zebrafish (males and females) at single neuron resolution. We demonstrate that single neuron activity across the whole brain exhibits a loss of critical statistics during seizures, suggesting that microscale activity collectively drives macroscale dynamics away from criticality. We also construct spiking network models at the scale of the larval zebrafish brain, to demonstrate that only densely connected networks can drive brain-wide seizure dynamics away from criticality. Importantly, such dense networks also disrupt the optimal computational capacities of critical networks, leading to chaotic dynamics, impaired network response properties and sticky states, thus helping to explain functional impairments during seizures. This study bridges the gap between microscale neuronal activity and emergent macroscale dynamics and cognitive dysfunction during seizures.

SIGNIFICANCE STATEMENTEpileptic seizures are debilitating and impair normal brain function. It is unclear how the coordinated behavior of neurons collectively impairs brain function during seizures. To investigate this we perform fluorescence microscopy in larval zebrafish, which allows for the recording of whole-brain activity at single-neuron resolution. Using techniques from physics, we show that neuronal activity during seizures drives the brain away from criticality, a regime that enables both high and low activity states, into an inflexible regime that drives high activity states. Importantly, this change is caused by more connections in the network, which we show disrupts the ability of the brain to respond appropriately to its environment. Therefore, we identify key neuronal network mechanisms driving seizures and concurrent cognitive dysfunction.

 

Opioid overdoses within the United States continue to rise and have been negatively impacting the social and economic status of the country. In order to effectively allocate resources and identify policy solutions to reduce the number of overdoses, it is important to understand the geographical differences in opioid overdose rates and their causes. In this study, we utilized data on emergency department opioid overdose (EDOOD) visits to explore the county-level spatio-temporal distribution of opioid overdose rates within the state of Virginia and their association with aggregate socio-ecological factors. The analyses were performed using a combination of techniques including Moran’s I and multilevel modeling. Using data from 2016–2021, we found that Virginia counties had notable differences in their EDOOD visit rates with significant neighborhood-level associations: many counties in the southwestern region were consistently identified as the hotspots (areas with a higher concentration of EDOOD visits) whereas many counties in the northern region were consistently identified as the coldspots (areas with a lower concentration of EDOOD visits). In most Virginia counties, EDOOD visit rates declined from 2017 to 2018. In more recent years (since 2019), the visit rates showed an increasing trend. The multilevel modeling revealed that the change in clinical care factors (i.e., access to care and quality of care) and socio-economic factors (i.e., levels of education, employment, income, family and social support, and community safety) were significantly associated with the change in the EDOOD visit rates. The findings from this study have the potential to assist policymakers in proper resource planning thereby improving health outcomes. 

DNA glycosylase MutY plays a critical role in suppression of mutations resulted from oxidative damage, as highlighted by cancer-association of the human enzyme. MutY requires a highly conserved catalytic Asp residue for excision of adenines misinserted opposite 8-oxo-7,8-dihydroguanine (OG). A nearby Asn residue hydrogen bonds to the catalytic Asp in structures of MutY and its mutation to Ser is an inherited variant in human MUTYH associated with colorectal cancer. We captured structural snapshots of N146S Geobacillus stearothermophilus MutY bound to DNA containing a substrate, a transition state analog and enzyme-catalyzed abasic site products to provide insight into the base excision mechanism of MutY and the role of Asn. Surprisingly, despite the ability of N146S to excise adenine and purine (P) in vitro, albeit at slow rates, N146S-OG:P complex showed a calcium coordinated to the purine base altering its conformation to inhibit hydrolysis. We obtained crystal structures of N146S Gs MutY bound to its abasic site product by removing the calcium from crystals of N146S-OG:P complex to initiate catalysis in crystallo or by crystallization in the absence of calcium. The product structures of N146S feature enzyme-generated β-anomer abasic sites that support a retaining mechanism for MutY-catalyzed base excision.

 

ABSTRACT Microbe-mediated transformations of arsenic (As) often require As to be taken up into cells prior to enzymatic reaction. Despite the importance of these microbial reactions for As speciation and toxicity, understanding of how As bioavailability and uptake are regulated by aspects of extracellular water chemistry, notably dissolved organic matter (DOM), remains limited. Whole-cell biosensors utilizing fluorescent proteins are increasingly used for high-throughput quantification of the bioavailable fraction of As in water. Here, we present a mathematical framework for interpreting the time series of biosensor fluorescence as a measure of As uptake kinetics, which we used to evaluate the effects of different forms of DOM on uptake of trivalent arsenite. We found that thiol-containing organic compounds significantly inhibited uptake of arsenite into cells, possibly through the formation of aqueous complexes between arsenite and thiol ligands. While there was no evidence for competitive interactions between arsenite and low-molecular-weight neutral molecules (urea, glycine, and glyceraldehyde) for uptake through the aquaglyceroporin channel GlpF, which mediates transport of arsenite across cell membranes, there was evidence that labile DOM fractions may inhibit arsenite uptake through a catabolite repression-like mechanism. The observation of significant inhibition of arsenite uptake at DOM/As ratios commonly encountered in wetland pore waters suggests that DOM may be an important control on the microbial uptake of arsenite in the environment, with aspects of DOM quality playing an important role in the extent of inhibition. IMPORTANCE The speciation and toxicity of arsenic in environments like rice paddy soils and groundwater aquifers are controlled by microbe-mediated reactions. These reactions often require As to be taken up into cells prior to enzymatic reaction, but there is limited understanding of how microbial arsenic uptake is affected by variations in water chemistry. In this study, we explored the effect of dissolved organic matter (DOM) quantity and quality on microbial As uptake, with a focus on the role of thiol functional groups that are well known to form aqueous complexes with arsenic. We developed a quantitative framework for interpreting fluorescence time series from whole-cell biosensors and used this technique to evaluate effects of DOM on the rates of microbial arsenic uptake. We show that thiol-containing compounds significantly decrease rates of As uptake into microbial cells at environmentally relevant DOM/As ratios, revealing the importance of DOM quality in regulating arsenic uptake, and subsequent biotransformation, in the environment. 

The global human footprint has fundamentally altered wildfire regimes, creating serious consequences for human health, biodiversity, and climate. However, it remains difficult to project how long-term interactions among land use, management, and climate change will affect fire behavior, representing a key knowledge gap for sustainable management. We used expert assessment to combine opinions about past and future fire regimes from 99 wildfire researchers. We asked for quantitative and qualitative assessments of the frequency, type, and implications of fire regime change from the beginning of the Holocene through the year 2300.

Respondents indicated some direct human influence on wildfire since at least ~ 12,000 years BP, though natural climate variability remained the dominant driver of fire regime change until around 5,000 years BP, for most study regions. Responses suggested a ten-fold increase in the frequency of fire regime change during the last 250 years compared with the rest of the Holocene, corresponding first with the intensification and extensification of land use and later with anthropogenic climate change. Looking to the future, fire regimes were predicted to intensify, with increases in frequency, severity, and size in all biomes except grassland ecosystems. Fire regimes showed different climate sensitivities across biomes, but the likelihood of fire regime change increased with higher warming scenarios for all biomes. Biodiversity, carbon storage, and other ecosystem services were predicted to decrease for most biomes under higher emission scenarios. We present recommendations for adaptation and mitigation under emerging fire regimes, while recognizing that management options are constrained under higher emission scenarios.

The influence of humans on wildfire regimes has increased over the last two centuries. The perspective gained from past fires should be considered in land and fire management strategies, but novel fire behavior is likely given the unprecedented human disruption of plant communities, climate, and other factors. Future fire regimes are likely to degrade key ecosystem services, unless climate change is aggressively mitigated. Expert assessment complements empirical data and modeling, providing a broader perspective of fire science to inform decision making and future research priorities.

 

Volumetric additive manufacturing (VAM) enables rapid printing into a wide range of materials, offering significant advantages over other printing technologies, with a lack of inherent layering of particular note. However, VAM suffers from striations, similar in appearance to layers, and similarly limiting applications due to mechanical and refractive index inhomogeneity, surface roughness, etc. We hypothesize that these striations are caused by a self-written waveguide effect, driven by the gelation material nonlinearity upon which VAM relies, and that they are not a direct recording of non-uniform patterning beams. We demonstrate a simple and effective method of mitigating striations via a uniform optical exposure added to the end of any VAM printing process. We show this step to additionally shorten the period from initial gelation to print completion, mitigating the problem of partially gelled parts sinking before print completion, and expanding the range of resins printable in any VAM printer.