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The Klebsormidiophyceae are a class of green microalgae observed globally in both freshwater and terrestrial habitats. Morphology‐based classification schemes of this class have been shown to be inadequate due to the simple morphology of these algae, the tendency of morphology to vary in culture versus field conditions, and rampant morphological homoplasy. Molecular studies revealing cryptic diversity have renewed interest in this group. We sequenced the complete chloroplast genomes of a broad series of taxa spanning the known taxonomic breadth of this class. We also sequenced the chloroplast genomes of three strains ofStreptofilum, a recently discovered green algal lineage with close affinity to the Klebsormidiophyceae. Our results affirm the previously hypothesized polyphyly of the genusKlebsormidiumas well as the polyphyly of the nominal species in this genus,K. flaccidum. Furthermore, plastome sequences strongly support the status ofStreptofilumas a distinct, early‐diverging lineage of charophytic algae sister to a clade comprising Klebsormidiophyceae plus Phragmoplastophyta. We also uncovered major structural alterations in the chloroplast genomes of species inKlebsormidiumthat have broad implications regarding the underlying mechanisms of chloroplast genome evolution.

 

We used high-resolution [Cii] 158μm mapping of two nebulae IC 59 and IC 63 from SOFIA/upGREAT in conjunction with ancillary data of the gas, dust, and polarization to probe the kinematics, structure, and magnetic properties of their photodissociation regions (PDRs). The nebulae are part of the Sh 2-185 Hiiregion that is illuminated by the B0 IVe starγCas. The velocity structure of each PDR changes with distance fromγCas, which is consistent with driving by the radiation. Based on previous far-ultraviolet (FUV) flux measurements of, and the known distance to,γCas, along with the predictions of 3D distances to the clouds, we estimated the FUV radiation field strength (G₀) at the clouds. Assuming negligible extinction between the star and clouds, we find their 3D distances fromγCas. For IC 63, our results are consistent with earlier estimates of distance from Andersson et al., locating the cloud at ∼2 pc fromγCas at an angle of 58° to the plane of the sky behind the star. For IC 59, we derive a distance of 4.5 pc at an angle of 70° in front of the star. We do not detect any significant correlation between the orientation of the magnetic field and the velocity gradients of [Cii] gas, which indicates a moderate magnetic field strength. The kinetic energy in IC 63 is estimated to be an order of 10 higher than the magnetic energies. This suggests that kinetic pressure in this nebula is dominant.

 

The Maine Department of Marine Resources (MEDMR) is a state agency tasked with developing, conserving, researching, and promoting commercial and recreational marine fisheries across Maine’s vast coastline. Close collaborations with industry members in each of the 30 or more fisheries that support Maine’s coastal economy are central to MEDMR’s efforts to address this suite of tasks. Here we reflect on recent decades of MEDMR's work and demonstrate how MEDMR fisheries research programmes are preparing for an uncertain future through the lens of three broadly applicable climate-driven challenges: (1) a rapidly changing marine ecosystem; (2) recommendations driven by state and federal climate initiatives; and (3) the need to share institutional knowledge with a new generation of marine resource scientists. We do this by highlighting our scientific and co-management approach to coastal Maine fisheries that have prospered, declined, or followed a unique trend over the last 25+ years. We use these examples to illustrate our lessons learned when studying a diverse array of fisheries, highlight the importance of collaborations with academia and the commercial fishing industry, and share our recommendations to marine resource scientists for addressing the climate-driven challenges that motivated this work.

 

Photodissociation regions (PDRs), where the (far-)ultraviolet light from hot young stars interact with the gas in surrounding molecular clouds, provide laboratories for understanding the nature and role of feedback by star formation on the interstellar medium. While the general nature of PDRs is well understood—at least under simplified conditions—the detailed dynamics and chemistry of these regions, including gas clumping, evolution over time, etc., can be very complex. We present interferometric observations of the 21 cm atomic hydrogen line, combined with [Cii] 158μm observations, toward the nearby reflection nebula IC 63. We find a clumpy Histructure in the PDR, and a ring morphology for the Hiemission at the tip of IC 63. We further unveil kinematic substructure, of the order of 1 km s⁻¹, in the PDR layers and several legs that will disperse IC 63 in <0.5 Myr. We find that the dynamics in the PDR explain the observed clumpy Hidistribution and lack of a well-defined Hi/H₂transition front. However, it is currently not possible to conclude whether Hiself-absorption and nonequilibrium chemistry also contribute to this clumpy morphology and missing Hi/H₂transition front.

 

The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census data alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings. 

The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatiotemporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. To overcome the sparsity of training data and to address the dynamic correlation of the disease, we propose clustering-based training for high-resolution forecasting. The methods help us to identify the similar trends of certain groups of regions due to various spatio-temporal effects. We examine the proposed method for forecasting weekly COVID-19 new confirmed cases at county-, state-, and country-level. A comprehensive comparison between different time series models in COVID-19 context is conducted and analyzed. The results show that simple deep learning models can achieve comparable or better performance when compared with more complicated models. We are currently integrating our methods as a part of our weekly forecasts that we provide state and federal authorities. 

This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lockdown, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-os between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of lockdown. Sectors that are worst hit are not the labor-intensive sectors such as Agriculture and Construction, but the ones with high valued jobs such as Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown. 

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID- 19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines. 

We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel agent-based modeling approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatio-temporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56{110k infections, 3.2{5.4k hospitalizations, and 700{900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3{6 million fewer infections, 181{306k fewer hospitalizations, and 51{62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed. 

Abstract Bonding in the ground state of C $${}_{2}$$ 2 is still a matter of controversy, as reasonable arguments may be made for a dicarbon bond order of $$2$$ 2 , $$3$$ 3 , or $$4$$ 4 . Here we report on photoelectron spectra of the C $${}_{2}^{-}$$ 2 − anion, measured at a range of wavelengths using a high-resolution photoelectron imaging spectrometer, which reveal both the ground $${X}^{1}{\Sigma}_{\mathrm{g}}^{+}$$ X 1 Σ g + and first-excited $${a}^{3}{\Pi}_{{\mathrm{u}}}$$ a 3 Π u electronic states. These measurements yield electron angular anisotropies that identify the character of two orbitals: the diffuse detachment orbital of the anion and the highest occupied molecular orbital of the neutral. This work indicates that electron detachment occurs from predominantly $$s$$ s -like ( $$3{\sigma}_{\mathrm{g}}$$ 3 σ g ) and $$p$$ p -like ( $$1{\pi }_{{\mathrm{u}}}$$ 1 π u ) orbitals, respectively, which is inconsistent with the predictions required for the high bond-order models of strongly $$sp$$ s p -mixed orbitals. This result suggests that the dominant contribution to the dicarbon bonding involves a double-bonded configuration, with 2 $$\pi$$ π bonds and no accompanying $$\sigma$$ σ bond.