Oxygen Atom Insertion into Iron(II) Phenyl and Methyl Bonds: A Key Step for Catalytic Hydrocarbon Functionalization
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We have determined the 2.3-{angstrom}-resolution crystal structure of a myosin light chain domain, corresponding to one type found in sea scallop catch ('smooth') muscle. This structure reveals hinges that may function in the 'on' and 'off' states of myosin. The molecule adopts two different conformations about the heavy chain 'hook' and regulatory light chain (RLC) helix D. This conformational change results in extended and compressed forms of the lever arm whose lengths differ by 10 {angstrom}. The heavy chain hook and RLC helix D hinges could thus serve as a potential major and localized source of cross-bridge compliance during the contractile cycle. In addition, in one of the molecules of the crystal, part of the RLC N-terminal extension is seen in atomic detail and forms a one-turn alpha-helix that interacts with RLC helix D. This extension, whose sequence is highly variable in different myosins, may thus modulate the flexibility of the lever arm. Moreover, the relative proximity of the phosphorylation site to the helix D hinge suggests a potential role for conformational changes about this hinge in the transition between the on and off states of regulated myosins.
Within the last several years quantum machine learning (QML) has begun to mature; however, many open questions remain. Rather than review open questions, in this perspective piece I will discuss my view about how we should approach problems in QML. In particular I will list a series of questions that I think we should ask ourselves when developing quantum algorithms for machine learning. These questions focus on what the definition of quantum ML is, what is the proper quantum analogue of QML algorithms is, how one should compare QML to traditional ML and what fundamental limitations emerge when trying to build QML protocols. As an illustration of this process I also provide information theoretic arguments that show that amplitude encoding can require exponentially more queries to a quantum model to determine membership of a vector in a concept class than classical bit-encodings would require; however, if the correct analogue is chosen then both the quantum and classical complexities become polynomially equivalent. This example underscores the importance of asking ourselves the right questions when developing and benchmarking QML algorithms.
We explore the possibility of having a successful leptogenesis through oscillations between new sterile fermion states added to the Standard Model field content in a well motivated framework, naturally giving rise to the required mass splitting between the sterile states through a small total lepton number violation. We propose a framework with only two sterile states forming a pseudo-Dirac state, in which their mass difference as well as the smallness of the neutrino masses are due to two sources of lepton number violation with ΔL=2, corresponding to an Inverse Seesaw framework extended by a Linear Seesaw mass term. We also explore the pure Inverse Seesaw mechanism in its minimal version, requiring at least four new sterile states in order to comply with neutrino data. Our analytical and numerical studies reveal that one can have a successful leptogenesis at the temperature of the electroweak scale through oscillations between the two sterile states with a “natural” origin of the strong degeneracy in their mass spectrum. We also revisit the analytical expression of the baryon asymmetry of the Universe in the weak washout regime of this framework.
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This paper discusses why it is necessary for new lower cost PV modules to be tested using a reliability test sequence that goes beyond the Qualification test sequence now utilized for modules. Today most PV modules are warranted for 25 years, but the Qualification Test Sequence does not test for 25-year life. There is no accepted test protocol to validate a 25-year lifetime. This paper recommendsthe use of long term accelerated testing to compare now designs directly with older designs that have achieved long lifetimes in outdoor exposure. If the new designs do as well or better than the older ones, then it is likely that they will survive an equivalent length of time in the field.
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Belowground microbial communities strongly influence ecosystem function such that predicting function may rely on understanding ecological processes that assemble communities. Uncertainty remains, however, in what governs the relative contributions of different ecological processes. To help fill this knowledge gap we test the general hypothesis that both initial state and degree of change in environmental conditions govern the relative contributions of different ecological assembly processes. To do so we leveraged regional-scale nutrient and organic matter addition experiments and used soil organic matter (SOM) as a proxy of integrated soil environmental conditions. Consistent with our hypothesis, we found that both the initial amount of SOM and the degree of change in SOM—in response to nutrient addition—influenced the relative contributions of different ecological assembly processes. These influences were most clearly observed at the regional scale, suggesting potential scale dependence. More specifically, nutrient additions homogenized bacterial community composition due to enhanced influences of homogenizing dispersal when SOM content was initially high. In contrast, nutrient additions led to divergence in community composition due to variable selection when initial SOM was low and/or when SOM increased significantly in response to nutrient additions. Our findings indicate important connections among initial conditions, degree of change in environmental variables, and microbial community assembly processes that may influence ecosystem processes. These conceptual inferences highlight a need to strengthen connections between ecological theory and biogeochemical modeling.
ABSTRACT The concentrations of molybdenum (Mo) and 25 other metals were measured in groundwater samples from 80 wells on the Oak Ridge Reservation (ORR) (Oak Ridge, TN), many of which are contaminated with nitrate, as well as uranium and various other metals. The concentrations of nitrate and uranium were in the ranges of 0.1 μM to 230 mM and <0.2 nM to 580 μM, respectively. Almost all metals examined had significantly greater median concentrations in a subset of wells that were highly contaminated with uranium (≥126 nM). They included cadmium, manganese, and cobalt, which were 1,300- to 2,700-fold higher. A notable exception, however, was Mo, which had a lower median concentration in the uranium-contaminated wells. This is significant, because Mo is essential in the dissimilatory nitrate reduction branch of the global nitrogen cycle. It is required at the catalytic site of nitrate reductase, the enzyme that reduces nitrate to nitrite. Moreover, more than 85% of the groundwater samples contained less than 10 nM Mo, whereas concentrations of 10 to 100 nM Mo were required for efficient growth by nitrate reduction for two Pseudomonas strains isolated from ORR wells and by a model denitrifier, Pseudomonas stutzeri RCH2. Higher concentrations of Mo tended to inhibit the growth of these strains due to the accumulation of toxic concentrations of nitrite, and this effect was exacerbated at high nitrate concentrations. The relevance of these results to a Mo-based nitrate removal strategy and the potential community-driving role that Mo plays in contaminated environments are discussed.
The purpose of this study was to identify major parameters and physical processes that have greatest impacts on the near surface energy balance in the Arctic environment. The historical data set for the period of 1948 to 2004 from National Center for Atmospheric Research (NCAR) was used to generate atmospheric forcing data for this analysis. The CLM 4.0 (Community Land Model) was used for land simulations of the point grid cell located near Fairbanks, Alaska. A range of hydrogeologic and thermal soil properties and vegetation characteristics were defined for the vegetation and soil data. The current approach used in CLM was modified to simulate soil moisture to allow for more realistic water table representation. Multiple CLM sensitivity runs were analyzed with regard to their effects on the feedbacks to the atmospheric model. This analysis allowed for identifying major parameters and important physical processes with the potential to impact the climate either in the short or long term.
This paper introduces the development of a new software framework for research, design, and development of wind energy systems which is meant to 1) represent a full wind plant including all physical and nonphysical assets and associated costs up to the point of grid interconnection, 2) allow use of interchangeable models of varying fidelity for different aspects of the system, and 3) support system level multidisciplinary analyses and optimizations. This paper describes the design of the overall software capability and applies it to a global sensitivity analysis of wind turbine and plant performance and cost. The analysis was performed using three different model configurations involving different levels of fidelity, which illustrate how increasing fidelity can preserve important system interactions that build up to overall system performance and cost. Analyses were performed for a reference wind plant based on the National Renewable Energy Laboratory's 5-MW reference turbine at a mid-Atlantic offshore location within the United States.
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Most B-tree articles assume that all $\textit{N}$ keys have the same size $\textit{K}$, that $\textit{f = B/K}$ keys fit in a disk block, and therefore that the search cost is $O(\text{log}_{f+1}N) $ block transfers. When keys have variable size, B-tree operations have no nontrivial performance guarantees, however. This article provides B-tree-like performance guarantees on dictionaries that contain keys of different sizes in a model in which keys must be stored and compared as opaque objects. The resulting atomic-key dictionaries exhibit performance bounds in terms of the average key size and match the bounds when all keys are the same size. Atomic-key dictionaries can be built with minimal modification to the B-tree structure, simply by choosing the pivot keys properly. Further, this article describes both static and dynamic atomic-key dictionaries. In the static case, if there are $\textit{N}$ keys with average size $\textit{K}$, the search cost is $O(\lceil K/B\rceil \text{log}_{1 + \lceil K/B\rceil} N)$ expected transfers. It is not possible to transform these expected bounds into worst-case bounds. The cost to build the tree is $\textit{O(NK)}$ operations and $\textit{O(NK/B)}$ transfers if all keys are presented in sorted order. If not, the cost is the sorting cost. For the dynamic dictionaries, the amortized cost to insert a key $\textit{κ}$ of arbitrary length at an arbitrary rank is dominated by the cost to search for $\textit{κ}$. Specifically, the amortized cost to insert a key $\textit{κ}$ of arbitrary length and random rank is $O(\lceil K/B\rceil log_{1+ \lceil B/K\rceil } N + |κ|/B)$ transfers. A dynamic-programming algorithm is shown for constructing a search tree with minimal expected cost. This article also gives a cache-oblivious static atomic-key B-tree, which achieves the same asymptotic performance as the static B-tree dictionary, mentioned previously. A cache-oblivious data structure or algorithm is not parameterized by the block size $\textit{B}$ or memory size $\textit{M}$ in the memory hierarchy; rather, it is universal, working simultaneously for all possible values of $\textit{B}$ or $\textit{M}$. On a machine with block size $\textit{B}$, if there are $\textit{N}$ keys with average size $\textit{K}$, search operations costs $O(\lceil K/B \rceil log_{1+ \lceil B/K \rceil} N)$ block transfers in expectation. This cache-oblivious layout can be built in $\textit{O(N}\text{log}\textit{(NK))}$ processor operations.
Cyber landscape evolves rapidly. Internet of Things (IoT) and Edge Computing (EC) have rapidly become an integral part of the modern computing infrastructure. It is expected that there will be more than 50 billion active and connected IoT devices by 2025 [1]. Pervasive IoT/EC creates unprecedented opportunities bridging the gap between previously segregated cyber and physical spaces. However, this progress also brings along new security challenges. IoT devices typically have limited computation, communication, and storage resources. This leads to security architecture designs such as using symmetric keys for group communication. While secure and efficient in stable network settings, symmetric key solutions are ill-adapted for IoT's highly dynamic device mobility behavior and frequent group membership turnover. Whenever IoT members leave a group, the known symmetric keys cannot be made forgotten, posing a serious vulnerability. This leads to frequent re-groupings that require expensive re-authentication, key regeneration, and key redistribution in order to maintain IoT/EC security. We present a novel symmetric key management framework that integrate an Incremental Threshold Scheme (ITS) cryptographical function into communication protocol's key rotation mechanism to allow for secure and efficient symmetric key communication group member node revocation. This ITS-enabled key management framework alleviates the need of frequent and expensive re-grouping and re-keying needed by today's large and dynamic IoT/EC operations. We further applied this ITS-enabled key management framework to a distributed IoT/EC-integrated publish and subscribe framework for applicability validation.