Concluding your structuretofunction gap for LRRK2

We report a variety of manganese-based catalysts containing both chelating diphosphine (bis(diphenylphosphino)methane (dppm 1, 2, and 7) or 1,2-bis(diphenylphosphino)ethane (dppe 3)), and mixed-donor phosphinoamine (2-(diphenylphosphino)ethylamine (dppea 4-6)) ligands for the upgrading of ethanol and methanol to the advanced biofuel isobutanol. These catalysts show moderate selectivity up to 74% along with turnover numbers greater than 100 over 90 h, with catalyst 2 supported by dppm demonstrating superior performance. The positive effect of substituting the ligand backbone was also displayed with a catalyst supported by C-phenyl-substituted dppm (8) having markedly improved performance compared to the parent dppm catalysts. Catalysts supported by the phosphinoamine ligand dppea are also active for the upgrading of ethanol to n-butanol. These results show that so-called PNP-pincer ligands are not a prerequisite for the use of manganese catalysts in Guerbet chemistry and that simple chelates can be used effectively.We assess the conditional relationship in the time-frequency domain between the return on S&P 500 and confirmed cases and deaths by COVID-19 in Hubei, China, countries with record deaths and the world, for the period from January 29 to June 30, 2020. Methodologically, we follow Aguiar-Conraria et al. (2018), by using partial coherencies, phase-difference diagrams, and gains. We also perform a parametric test for Granger-causality in quantiles developed by Troster (2018). We find that short-term cycles of deaths in Italy in the first days of March, and soon afterwards, cycles of deaths in the world are able to lead out-of-phase US stock market. We find that low frequency cycles of the US market index in the first half of April are useful to anticipate in an anti-phasic way the cycles of deaths in the US. We also explore sectoral contagion, based on dissimilarities, Granger causality and partial coherencies between S&P sector indices. Our findings, such as the strategic role of the energy sector, which first reacted to the pandemic, or the evidence about predictability of the Telecom cycles, are useful to tell the history of the pass-through of this recent health crises across the sectors of the US economy.We have obtained graph-theoretically based topological indices for the characterization of certain graph theoretical networks of biochemical interest. We have derived certain distance, degree and eccentricity based topological indices for various k-level hypertrees and corona product of hypertrees. We have also pointed out errors in a previous study. The validity of our results is supported by computer codes for the respective indices. Several biochemical applications are pointed out.Socio-economic factors could impact how epidemics spread. In this study, we investigated the possible effect of several local socio-economic factors on the case count and time course of confirmed Covid-19 cases and Covid-19-related deaths across the twenty one counties of New Jersey. Socio-economic and geographic factors considered included population, percentage of elders in the population, percentage of low-income households, access to food and health facilities and distance to New York. We found that the counties could be clustered into three groups based on (a) the case totals, (b) the total number of deaths, (c) the time course of the cases and (d) the time course of the deaths. The four groupings were very similar to one another and could all be largely explained by the county population, the percentage of low-income population, and the distance of the county from New York. As for food and health factors, the numbers of local restaurants and pharmacies significantly influenced the total number of cases and deaths as well as the epidemic's evolution. In particular, the number of cases and deaths showed a decrease with the number of McDonald's within the county in contrast to other fast-food or non-fast food restaurants. Overall, our study found that the evolution of the epidemic was influenced by certain socio-economic factors, which could be helpful for the formulation of public health policies.Pharmaceutical supply chain (PSC) is one of the most important healthcare supply chains and the recent pandemic (COVID-19) has completely proved it. Also, the environmental and social impacts of PSCs are undeniable due to the daily entrance of a large amount of pharmaceutical waste into the environment. However, studies on closed-loop PSCs (CLPSC) are rarely considered real-world requirements such as competition among diverse brands of manufacturers, the dependency of customers' demand on products' price and quality, and diverse reverse flows of end-of-life medicines. In this study, a scenario-based Multi-Objective Mixed-Integer Linear Programming model is developed to design a sustainable CLPSC, which investigates the reverse flows of expired medicines as three classes (must be disposed of, can be remanufactured and can be recycled). To study the competitive market and deal with demand uncertainty, a novel scenario-based game theory model is proposed. The demand function for each brand depends on the price and quality provided. Selleck MT-802 Then, a hybrid solution approach is provided by combining the LP-metrics method with a heuristic algorithm. Furthermore, a real case study is investigated to evaluate the application of the model. Finally, sensitivity analysis and managerial insights are provided. The numerical results show that the proposed classification of reverse flows leads to proper waste management, making money, and reducing both disposal costs and raw material usage. Moreover, competition increases PSCs performance and improves the supply of products to pharmacies.
The online version contains supplementary material available at 10.1007/s10479-021-03961-0.
The online version contains supplementary material available at 10.1007/s10479-021-03961-0.Many government strategies to reduce the spread of Novel Coronavirus (COVID-19) involved unprecedented restrictions on personal movement, disrupting social and economic norms. Although generally well-received in Australia, community frustration regarding these restrictions appeared to diverge across political lines. Therefore, we examined the unique effects of the ideological subfactors of Right-Wing Authoritarianism (RWA; Aggression, Submission and Conventionalism) and Social Dominance Orientation (SDO; Dominance and Anti-egalitarianism) in predicting perceived personal threat of COVID-19, and support for and reactance to government restrictions, in Australian residents across two separate samples (S1 N = 451, S2 N = 838). COVID-19 threat was positively predicted by Submission, and negatively by Conventionalism, and Anti-egalitarianism. Support for restrictions was also positively predicted by Submission, and negatively by Conventionalism, Dominance, and Anti-egalitarianism. Reactance to government restrictions was negatively predicted by Submission, and positively by Conventionalism, Dominance, and Anti-egalitarianism. These findings suggest that right-wing ideological subfactors contribute to the one's perception of COVID-19 threat and government restrictions differentially.A robot modeled as a deterministic finite automaton has to build a structure from material available to it. The robot navigates in the infinite oriented grid Z × Z . Some cells of the grid are full (contain a brick) and others are empty. The subgraph of the grid induced by full cells, called the shape, is initially connected. The (Manhattan) distance between the furthest cells of the shape is called its span. The robot starts at a full cell. It can carry at most one brick at a time. At each step it can pick a brick from a full cell, move to an adjacent cell and drop a brick at an empty cell. The aim of the robot is to construct the most compact possible structure composed of all bricks, i.e., a nest. That is, the robot has to move all bricks in such a way that the span of the resulting shape be the smallest. Our main result is the design of a deterministic finite automaton that accomplishes this task and subsequently stops, for every initially connected shape, in time O ( s n ) , where s is the span of the initial shape and n is the number of bricks. We show that this complexity is optimal.Flip graphs are a ubiquitous class of graphs, which encode relations on a set of combinatorial objects by elementary, local changes. Skeletons of associahedra, for instance, are the graphs induced by quadrilateral flips in triangulations of a convex polygon. For some definition of a flip graph, a natural computational problem to consider is the flip distance Given two objects, what is the minimum number of flips needed to transform one into the other? We consider flip graphs on orientations of simple graphs, where flips consist of reversing the direction of some edges. More precisely, we consider so-called α -orientations of a graph G, in which every vertex v has a specified outdegree α ( v ) , and a flip consists of reversing all edges of a directed cycle. We prove that deciding whether the flip distance between two α -orientations of a planar graph G is at most two is NP-complete. This also holds in the special case of perfect matchings, where flips involve alternating cycles. This problem amounts to finding geodesics on the common base polytope of two partition matroids, or, alternatively, on an alcoved polytope. It therefore provides an interesting example of a flip distance question that is computationally intractable despite having a natural interpretation as a geodesic on a nicely structured combinatorial polytope. We also consider the dual question of the flip distance between graph orientations in which every cycle has a specified number of forward edges, and a flip is the reversal of all edges in a minimal directed cut. In general, the problem remains hard. However, if we restrict to flips that only change sinks into sources, or vice-versa, then the problem can be solved in polynomial time. Here we exploit the fact that the flip graph is the cover graph of a distributive lattice. This generalizes a recent result from Zhang et al. (Acta Math Sin Engl Ser 35(4)569-576, 2019).In this paper, we convert the recent COVID-19 model with the use of the most influential theories, such as variable fractional calculus and fuzzy theory. We propose the fuzzy variable fractional differential equation for the COVID-19 model in which the variable fractional-order derivative is described using the Caputo-Fabrizio in the Caputo sense. Furthermore, we provide the results on the existence and uniqueness using Lipschitz conditions. Also, discuss the stability analysis of the present new COVID-19 model by employing Hyers-Ulam stability.The COVID-19 pandemic has resulted in many changes in the way research is conducted. Some specific groups (e.g. women) and activities (e.g. teaching) may have been disproportionally affected. Our aim was to assess the impact of the COVID-19 pandemic on animal behaviour and welfare researchers' work experience and productivity, focussing on gender, care role, career stage and teaching load. An online survey asked researchers about childcare, research and teaching load and associated changes due to the pandemic, among others, and included the Perceived Stress Scale (PSS) and the Inventory of Socially Supportive Behaviours (ISSB). From June-July 2020, 117 completed responses were received from 28 countries. Time available for writing papers and grants either increased (36 %), decreased (31 %) or these tasks were halted completely (12 %). Perceived productivity was significantly lower for caregivers (P less then 0.001) and for men as compared to women (P less then 0.001); and low productivity was associated with more stress (higher PSS P less then 0.