Analytical Modeling of Thermodynamic and Transport Anomalies of Water

The structures and properties of biomolecules like proteins, nucleic acids, and membranes depend on water. Water is also very important in industry. Overall, water is an unusual substance with more than 70 anomalous properties. The understanding of water is advancing significantly due to the theoretical and computational modeling. There are different kinds of models, models with fine-scale properties and increasing structural detail with increasing computational expense, and simple models, which focus on global properties of water like thermodynamics, phase diagram and are less computationally expensive. Simplified models give a better understanding of water in ways that complement more complex models. Here, we review analytical modelling of properties of water on different levels, the two- and three-dimensional Mercedes– Benz (MB) models of water and experimental water.

EXPERIMENTAL ANALYSIS OF BEAM-TO-COLUMN JOINTS 1 EQUIPPED WITH SPRAYED ALUMINIUM FRICTION DAMPERS

In this paper, the results of an experimental analysis regarding beam-to-column joints equipped with friction dampers is presented. Even though the overall concept is not new, the connection structural detail and the friction pad material are different from previous proposals. In particular, the beam is connected to the column with a classical fixed T-stub fastening the upper flange and a friction damper located at the beam lower flange. The friction damper is composed of a stack of steel plates conceived to assure symmetrical friction. The friction pads are made of steel plates coated with thermally sprayed aluminium. The friction damper is designed in order to slide for a force level equal to or lower than the ratio between the nominal flexural resistance of the connected beam and the lever arm, i.e. the distance between the top T-stub and the friction damper. In this way, it is possible to obtain connections able to dissipate the seismic input energy almost without any damage to the steel elements, provided that all the joint components are designed with sufficient over-strength with respect to the actions corresponding to the friction damper sliding force. In this paper, such approach is validated reporting the results of an experimental campaign.

Structural basis for inhibition of the type I-F CRISPR–Cas surveillance complex by AcrIF4, AcrIF7 and AcrIF14

CRISPR–Cas systems are adaptive immune systems in bacteria and archaea to defend against mobile genetic elements (MGEs) and have been repurposed as genome editing tools. Anti-CRISPR (Acr) proteins are produced by MGEs to counteract CRISPR–Cas systems and can be used to regulate genome editing by CRISPR techniques. Here, we report the cryo-EM structures of three type I-F Acr proteins, AcrIF4, AcrIF7 and AcrIF14, bound to the type I-F CRISPR–Cas surveillance complex (the Csy complex) from Pseudomonas aeruginosa. AcrIF4 binds to an unprecedented site on the C-terminal helical bundle of Cas8f subunit, precluding conformational changes required for activation of the Csy complex. AcrIF7 mimics the PAM duplex of target DNA and is bound to the N-terminal DNA vise of Cas8f. Two copies of AcrIF14 bind to the thumb domains of Cas7.4f and Cas7.6f, preventing hybridization between target DNA and the crRNA. Our results reveal structural detail of three AcrIF proteins, each binding to a different site on the Csy complex for inhibiting degradation of MGEs.

Protein Conformational States—A First Principles Bayesian Method

Automated identification of protein conformational states from simulation of an ensemble of structures is a hard problem because it requires teaching a computer to recognize shapes. We adapt the naïve Bayes classifier from the machine learning community for use on atom-to-atom pairwise contacts. The result is an unsupervised learning algorithm that samples a ‘distribution’ over potential classification schemes. We apply the classifier to a series of test structures and one real protein, showing that it identifies the conformational transition with >95% accuracy in most cases. A nontrivial feature of our adaptation is a new connection to information entropy that allows us to vary the level of structural detail without spoiling the categorization. This is confirmed by comparing results as the number of atoms and time-samples are varied over 1.5 orders of magnitude. Further, the method’s derivation from Bayesian analysis on the set of inter-atomic contacts makes it easy to understand and extend to more complex cases.

Anatomical Detail and Accuracy of the Pernkopf Atlas and Examples of Clinical Impact

The high fidelity anatomical structural detail seen in the Pernkopf atlas remains unmatched in other references, including surgical anatomy atlases. An example of serial dissection illustrations are examined herein, in relation to an anatomically based clinical question. The question is about radiofrequency nerve ablation, an image-guided procedure that provides a non-opioid alternative to treat joint pain. To perform these image-guided procedures effectively, the location and course of the nerve(s) being targeted is very important. Although the patient had good pain relief, the clinician was concerned about the patient's loss of sensation around the anus following an ablation procedure of the nerves innervating the sacroiliac joint, and asked for more information about the clunial nerves and their relevance to this procedure. The anatomical illustrations in the Pernkopf atlas are highly detailed and drawn from serially dissected specimens from the skin superficially to the level of the origin of the nerves from the vertebral column deeply. Tracing the clunial nerves through five serial illustrations provided the necessary anatomical insight required to answer this clinical question for development of the ablation procedure. This atlas could play a significant role in educating future clinicians and surgeons and provide answers to anatomically related clinical quandaries. However, the atlas must always be used by first acknowledging its origins and history. Image credit: Table of Contents image provided by the Medical University of Vienna, MUW-Andruck-180ll-Seite-1.