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As the Zika virus protease is an essential and well-established target for the development of antiviral agents, we biochemically screened for inhibitors using a purified recombinantly expressed form of this enzyme. As a result, we were able to identify 10 new Zika virus protease inhibitors. These compounds are natural products and showed strong inhibition in the biochemical assays. Inhibitory constants values for the compounds ranged from 5 nM to 8 μM. Among the most potent inhibitors are flavonoids like irigenol hexa-acetate (K

The elucidation of mechanisms behind the thermostability of proteins is extremely important both from the theoretical and applied perspective. Here we report the crystal structure of methylenetetrahydrofolate dehydrogenase (MTHFD) from Thermus thermophilus HB8, a thermophilic model organism. Molecular dynamics trajectory analysis of this protein at different temperatures (303 K, 333 K and 363 K) was compared with homologous proteins from the less temperature resistant organism Thermoplasma acidophilum and the mesophilic organism Acinetobacter baumannii using several data reduction techniques like principal component analysis (PCA), residue interaction network (RIN) analysis and rotamer analysis. These methods enabled the determination of important residues for the thermostability of this enzyme. The description of rotamer distributions by Gini coefficients and Kullback-Leibler (KL) divergence both revealed significant correlations with temperature. The emerging view seems to indicate that a static salt bridge/charged residue network plays a fundamental role in the temperature resistance of Thermus thermophilus MTHFD by enhancing both electrostatic interactions and entropic energy dispersion. Furthermore, this analysis uncovered a relationship between residue mutations and evolutionary pressure acting on thermophilic organisms and thus could be of use for the design of future thermostable enzymes.

What is sustainability? Sustainability is a concept that can be defined in many ways depending upon a society's perception of current material needs and the actual material needs of future generations. Much of our ability to achieve sustainability entails developing indicators and measurements that will guide us to this goal. This paper suggests that we can strengthen the prediction of sustainability indicators by adopting a “multiple equilibria” approach for a more effective decision-making process in various sectors of the economy, in ecosystem protection, or in political arenas. There is an emerging need for further development of predictive mathematical models of system sustainability over economic growth models for sustainable resource measurement and management. The objective of this paper is to use computer modeling and differential equations to simulate the “multiple equilibria” of a 3 variable real world system. In our study, we tested the theoretical validity of “multiple equilibria” sustainability modeling through simulated measurements of precipitation and nitrogen runoff into a hypothetical lake. As a quantitative tool to model, the “multiple equilibria” techniques can have tremendous predictive power for business leaders, political decision makers, and environmental scientists, and assist in better management of ecological, economic, and material resources in short-term and long-term end-use scenarios.

Background Understanding the determinants of protein thermostability is very important both from the theoretical and applied perspective. One emerging view in thermostable enzymes seems to indicate that a salt bridge/charged residue network plays a fundamental role in their thermostability. Methods The structure of alkaline phosphatase (AP) from Thermus thermophilus HB8 was solved by X-ray crystallography at 2.1 A resolution. The obtained structure was further analyzed by molecular dynamics studies at different temperatures (303 K, 333 K and 363 K) and compared to homologous proteins from the cold-adapted organisms Shewanella sp. and Vibrio strain G15–21. To analyze differences in measures of dynamic variation, several data reduction techniques like principal component analysis (PCA), residue interaction network (RIN) analysis and rotamer analysis were used. Using hierarchical clustering, the obtained results were combined to determine residues showing high degree dynamical variations due to temperature jumps. Furthermore, dynamic cross correlation (DCC) analysis was carried out to characterize networks of charged residues. Results Top clustered residues showed a higher propensity for thermostabilizing mutations, indicating evolutionary pressure acting on thermophilic organisms. The description of rotamer distributions by Gini coefficients and Kullback–Leibler (KL) divergence both revealed significant correlations with temperature. DCC analysis revealed a significant trend to de-correlation of the movement of charged residues at higher temperatures. Significance The de-correlation of charged residues detected in Thermus thermophilus AP, highlights the importance of dynamic electrostatic network interactions for the thermostability of this enzyme.

The Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) pandemic represents a global challenge. SARS-CoV-2's ability to replicate in host cells relies on the action of its non-structural proteins, like its main protease (Mpro). This cysteine protease acts by processing the viruses' precursor polyproteins. As proteases, together with polymerases, are main targets of antiviral drug design, we here have performed biochemical high throughput screening (HTS) with recombinantly expressed SARS-CoV-2 Mpro. A fluorescent assay was used to identify inhibitors in a compound library containing known drugs, bioactive molecules and natural products. These screens led to the identification of 13 inhibitors with IC50 values ranging from 0.2 μM to 23 μM. The screens confirmed several known SARS-CoV Mpro inhibitors as inhibitors of SARS-CoV-2 Mpro, such as the organo-mercuric compounds thimerosal and phenylmercuric acetate. Benzophenone derivatives could also be identified among the most potent screening hits. Additionally, Evans blue, a sulfonic acid-containing dye, could be identified as an Mpro inhibitor. The obtained compounds could be of interest as lead compounds for the development of future SARS-CoV-2 drugs.

Vehicle collisions amount to a significant loss of life in America. Upward of 30,000 lives are lost each year in the United States alone. Hundreds of millions of dollars are spent each year on vehicle safety and roadway design in the United States, and many studies have been conducted on the causal factors for vehicle collisions. This study used artificial neural networks as a means to predict the occurrence of injury of a vehicle collision. The objective was to classify the levels of the "injury occurrence" in vehicle collisions, given certain input variables, including demographic and environmental factors involved in crashes. Using Neural Ware's Predict software, a back-propagation neural network model was constructed, trained, tested, and validated using data from the 2006 and 2007 Florida Traffic Crash Database. The neural network's computational power was iteratively increased by adding hidden layers to boost its predicting power until the performance began to plateau. Classification rates as well as Kolmogorov-Smirnov K-S statistical analysis were used to validate the model. A sensitivity analysis was then applied to find the level of contribution that the input variables had on the injury occurrence. Accident severity, number of lanes, and road surface conditions were found to have the most positive impacts on the injury occurrence. The implications of the study are discussed at the end of the article.

This study applied a backpropagation artificial neural network approach to investigate both the Human Factors Analysis and Classification System (HFACS)-related unsafe act tiers of factors and other non-HFACS factors in an attempt to recognize patterns for general aviation accident fatalities. Data were obtained from the HFACS database and extracted from the National Transportation Safety Board database from 1990 to 2002. Multiple neural network models were created and the best fit model was selected based on a sequence of criteria. A sensitivity analysis was performed on the validated model to rank the factors that lead to general aviation fatalities. Results are discussed and practical implications are given.

Vehicle collisions amount to a significant loss of life in America. This study used artificial neural networks as a means to predict the occurrence of injury of a vehicle collision. Using Neural Ware's Predict software a neural network structure was trained, tested, and validated using data from the 2006 and 2007 Florida Traffic Crash Database. The objective was to assess whether or not properly designed neural network architecture could adequately classify the levels of the "Injury Occurrence" output variable, given certain inputs such as demographic and environmental factors involved in crashes. A Kolmogorov-Smirnov statistical analysis was employed to objectively assess whether or not the neural network properly classified the levels of Injury Occurrence and to what extent. The artificial network's computational power was iteratively increased by adding hidden layers thus boosting its performance. a sensitivity analysis was used to find the level of contribution the input variables had on the "Injury Occurrence" output variable. Top three positive and negative most impacting factors were identified and the implications were discussed at the end of the paper. Language: en

SUMMARYThis paper presents a new algorithm for the control of a snake-like robot with passive joints and active wheels. Each segment has four autonomously driven and steered wheels. The algorithm approximates the ideal solution in which all wheels on a segment have the same centre of curvature with wheel speeds, providing cooperative redundancy. Each hitch point joining segments traverses the same path, which is determined by an operator, prescribing the path curvature and front hitch speed. The numerical algorithm developed in this paper is simulation tested against a previously derived analytical solution for a predetermined path. Further simulations are carried out to show the effects of changing curvature and front hitch speed on hitch path, wheel angles and wheel speeds for a one, two and three segment robot.

In this paper, we apply our approach of Neurohydrodynamics (NHD) to a Hopfield neural network by introducing a one-dimensional spacial diffusion term. This reaction-diffusion equation includes an auxiliary equation that “guides” the weights of the network using the divergence of neuron's activation amplitude, which we call the neuropotential. This guiding principle is similar to de Broglie's “pilot wave” interpretation for Quantum Mechanics or Turing's oracle for “human intuition” of a Turing machine. Finally, using a numerical derivation of the dynamical equations of one-dimensional Hopfield neural network, we include a simulation of the network so that we can discuss its behavior and future directions of NHD.

We propose to model learning of optimal decision rules using a mathematically rigorous descriptive model given by a modified set of Cohen-Grossberg neural network equations with reaction-diffusion processes in an environment of uncertainty. Our theory, which we call Neurohydrodynamics, naturally arises within the framework of neural networks while utilizing the foundations of Decision Field Theory (DFT) for describing the cognitive processes of the mammalian brain in the decision-making processes. Human cognition and intelligence requires more than an algorithmic description by a formal set of rules for its operation; it must possess what Alan Turing called an uncomputable human “intuition” (oracle) as a guide for decision-making processes. We draw an analogy with an idea from Quantum Hydrodynamics, namely, that a “pilot wave” guides quantum mechanical particles along a deterministic path by stochastic “forces” naturally arising from Schrodinger's wave equation. This type of equation was also investigated by Turing, and the reaction-diffusion processes of real neurons have been shown to aid in pattern formation while exhibiting self-organization. Because searching over all courses of action is costly, in both resources and time, we seek to include a mechanism that shortcuts the decision-making processes as described by DFT. Some empirical research has determined that diffusion does occur in the cognitive processing of real human brains. We propose a model for high-level decision processes by combining diffusion with other mechanisms (e.g., adaptive resonance and neuromodulation) for the interactions between different brain regions. For dynamic decision-making tasks, the diffusion processes within certain parts of the frontal lobes and basal ganglia are assumed to interact in hierarchical networks that integrate emotion and cognition incorporating both heuristic and deliberative decision rules.