SILVR: Guided Diffusion for Molecule Generation

Computationally generating novel synthetically accessible compounds with high affinity and low toxicity is a great challenge in drug design. Machine-learning models beyond conventional pharmacophoric methods have shown promise in generating novel small molecule compounds, but require significant tuning for a specific protein target. Here, we introduce a method called selective iterative latent variable refinement (SILVR) for conditioning an existing diffusion-based equivariant generative model without retraining. The model allows the generation of new molecules that fit into a binding site of a protein based on fragment hits. We use the SARS-CoV-2 Main protease fragments from Diamond X-Chem that form part of the COVID Moonshot project as a reference dataset for conditioning the molecule generation. The SILVR rate controls the extent of conditioning and we show that moderate SILVR rates make it possible to generate new molecules of similar shape to the original fragments, meaning that the new molecules fit the binding site without knowledge of the protein. We can also merge up to 3 fragments into a new molecule without affecting the quality of molecules generated by the underlying generative model. Our method is generalizable to any protein target with known fragments and any diffusion-based model for molecule generation.Comment: paper, 20 paper, 11 figure

Model Updating for Loading Capacity Estimation of Concrete Structures using Ambient Vibration

This paper presents a model updating approach for determining the loading capacity of a concrete structure utilising measured ambient vibration responses. The proposed method uses Operational Modal Analysis (OMA) with the Enhanced Frequency Domain Decomposition (EFDD) technique to identify the natural frequencies and mode shapes of an experimental replica specimen of a Sydney Harbour Bridge concrete jack arch component. For vibration testing, the structure is excited with ambient vibration recordings from the actual Sydney Harbour Bridge using a vibro tactile transducer. The vibration responses of the structure are measured using an array of strategically placed accelerometers. A numerical model is developed and updated using the vibrational characteristics with the aim of estimating the load capacity of the structure. The results show that the proposed updating method using partitioning has great potential to be used for determining the loading capacity of a structure as part of a Structural Health Monitoring (SHM) system

An upwind vertex centred finite volume algorithm for computational contact mechanics

The Discrete Element Method (DEM) has been employed in recent years to simulate flexible protection structures undergoing dynamic loading due to its inherent aptitude for dealing with inertial effects and large deformations. The individual structural elements are discretized with an arbitrary number of discrete elements, connected by spring-like remote interactions. In this work, we implement this approach using the parallel bond contact model and compare the numerical results at different discretization intervals with the analytical solutions of classical beam theory. Successively, we use the same model to simulate the punching test of a steel wire mesh and quantify the influence of a different number of elements on the macroscopic response

Technical note: Continuous fluorescence-based monitoring of seawater pH in situ

Electrical conductivity (salinity), temperature and fluorescence-based measurements of pH were employed to examine diel fluctuations in seawater carbonate chemistry of surface waters in Sydney Harbour over two multiple-day periods. A proof-of-concept device employing the fluorescence-based technique provided a useful time series for pH. Alkalinity with pH and temperature were used to calculate the degree of calcite and aragonite saturation (ΩCa and ΩAr, respectively). Alkalinity was determined from a published alkalinity–salinity relationship. The fluctuations observed in pH over intervals of minutes to hours could be distinguished from background noise. While the stated phase angle resolution of the lifetime fluorometer translated into pH units was ±0.0028 pH units, the repeatability standard deviation of calculated pH was 0.007 to 0.009. Diel variability in pH, ΩAr and ΩCa showed a clear pattern that appeared to correlate with both salinity and temperature. Drift due to photodegradation of the fluorophore was minimized by reducing exposure to ambient light. The ΩCa and ΩAr fluctuated on a daily cycle. The net result of changes in pH, salinity and temperature combined to influence seawater carbonate chemistry. The fluorescence-based pH monitoring technique is simple, provides good resolution and is unaffected by moving parts or leaching of solutions over time. The use of optics is pressure insensitive, making this approach to ocean acidification monitoring well suited to deepwater applications.</p

Viral Networks: Connecting Digital Humanities and Medical History

This volume of original essays explores the power of network thinking and analysis for humanities research. Contributing authors are all scholars whose research focuses on a medical history topic—from the Black Death in fourteenth-century Provence to psychiatric hospitals in twentieth-century Alabama. The chapters take readers through a variety of situations in which scholars must determine if network analysis is right for their research; and, if the answer is yes, what the possibilities are for implementation. Along the way, readers will find practical tips on identifying an appropriate network to analyze, finding the best way to apply network analysis, and choosing the right tools for data visualization. All the chapters in this volume grew out of the 2018 Viral Networks workshop, hosted by the History of Medicine Division of the National Library of Medicine (NIH), funded by the Office of Digital Humanities of the National Endowment for the Humanities, and organized by Virginia Tech

Establishing research strategies, methodologies and technologies to link genomics and proteomics to seagrass productivity, community metabolism, and ecosystem carbon fluxes

A complete understanding of the mechanistic basis of marine ecosystem functioning is only possible through integrative and interdisciplinary research. This enables the prediction of change and possibly the mitigation of the consequences of anthropogenic impacts. One major aim of the European Cooperation in Science and Technology (COST) Action ES0609 “Seagrasses productivity. From genes to ecosystem management,” is the calibration and synthesis of various methods and the development of innovative techniques and protocols for studying seagrass ecosystems. During 10 days, 20 researchers representing a range of disciplines (molecular biology, physiology, botany, ecology, oceanography, and underwater acoustics) gathered at The Station de Recherches Sous-marines et Océanographiques (STARESO, Corsica) to study together the nearby Posidonia oceanica meadow. STARESO is located in an oligotrophic area classified as “pristine site” where environmental disturbances caused by anthropogenic pressure are exceptionally low. The healthy P. oceanica meadow, which grows in front of the research station, colonizes the sea bottom from the surface to 37 m depth. During the study, genomic and proteomic approaches were integrated with ecophysiological and physical approaches with the aim of understanding changes in seagrass productivity and metabolism at different depths and along daily cycles. In this paper we report details on the approaches utilized and we forecast the potential of the data that will come from this synergistic approach not only for P. oceanica but for seagrasses in general

Depth-specific fluctuations of gene expression and protein abundance modulate the photophysiology in the seagrass <i>Posidonia oceanica</i>

Here we present the results of a multiple organizational level analysis conceived to identify acclimative/adaptive strategies exhibited by the seagrass Posidonia oceanica to the daily fluctuations in the light environment, at contrasting depths. We assessed changes in photophysiological parameters, leaf respiration, pigments, and protein and mRNA expression levels. The results show that the diel oscillations of P. oceanica photophysiological and respiratory responses were related to transcripts and proteins expression of the genes involved in those processes and that there was a response asynchrony between shallow and deep plants probably caused by the strong differences in the light environment. The photochemical pathway of energy use was more effective in shallow plants due to higher light availability, but these plants needed more investment in photoprotection and photorepair, requiring higher translation and protein synthesis than deep plants. The genetic differentiation between deep and shallow stands suggests the existence of locally adapted genotypes to contrasting light environments. The depth-specific diel rhythms of photosynthetic and respiratory processes, from molecular to physiological levels, must be considered in the management and conservation of these key coastal ecosystems

Occupational Communication as Boundary Mechanism

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69000/2/10.1177_073088847400100404.pd