Exe Jpg Binder 2 5.epub
Gordon Neal
Our research is inspired by the observation that early life experiences have profound impact on the behavioral patterns, repertoire of subjective experiences and mental health outcomes later in life. To understand how experience shapes the brain, behavior, mental health and ultimately consciousness, we integrate approaches from artificial intelligence, structural biology, bioengineering, developmental and behavioral neuroscience and psychology/psychiatry.
The diverse cell types that make up complex organisms and the diverse cellular compartments within cells can often be distinguished by their specific molecular compositions. To facilitate access to various parts of cells and of organisms, we engineer molecular tools that are active in the presence of target molecules of interest. Our initial efforts have focused on nanobodies, antibody fragments derived from Camelid species. Nanobodies are ideal for biomedical applications due to their encoding of antigen-binding in a single polypeptide chain, and the relative ease with which they can be expressed inside cells. Using nanobodies as building blocks for synthetic biology, we demonstrated that one could build cell type-specific systems that are active only in specific cell populations expressing a target molecule. We applied these systems to repurpose transgenic GFP animal lines as tools for cell type-specific manipulation, bypassing the need to create traditional effector transgenic lines such as Cre- or Flp- expressing animals. We later demonstrated that nanobodies can be rendered conditionally stable by modifying their conserved and non-epitope binding framework regions, greatly simplifying delivery and enhancing generalizability. Going forward, we will enhance the generalizability of the conditional stability approach by systematically generating novel binders, optimizing engineering strategies and building bioinformatics resources for the community. We are further interested in expanding the conditionally stable strategy to other binder systems. Collectively, these efforts will result in a large panel of reagents, bioinformatics tools and generalizable engineering strategies suitable for any biomedical application that require target-specific sensing or manipulation across cells and organisms.
We aim to apply our engineered molecular sensors and effectors in our efforts to understand how animals learn about the world. Animals have the remarkable ability to home in on reward-causing actions and action sequences as a result of exploration and experience. However, the process by which animals assign credits to actions have been elusive, due to confounding factors in traditional operant conditioning paradigms. We overcame these obstacles by developing a closed loop reinforcement system that strips down reinforcement rules to their most minimal requirements (ex. Action > Reward, instead of Action sequences + Place / Cue / Manipulandum interactions > Reward). The closed loop system combines wireless inertial sensors, unsupervised behavioral classification and optogenetics to convert behavior detection into cell-type specific stimulation of dopamine neurons. Using this closed loop system in mice, we discovered behavioral dynamics behind how animals assign credit to specific actions and action sequences that lead to reward. Our findings point to the importance of the refinement process in homing in on the right actions, and appreciation for latent learning in more difficult action learning problems. Going forward, we aim to integrate state-of-the-art neural recording technologies (ex. miniscopes, neuropixels, fiber photometry) to probe the neural computations behind action credit assignment. These efforts have the potential to reveal neural populations, circuits, and behavioral patterns to target for diagnosis and therapy in neurological and psychiatric conditions where action learning is impaired (ex. rigid behavior in autism and personality disorders, obsessive-compulsive disorder, addiction, etc).
We are looking for individuals who are passionate about learning and growing and solving longstanding problems in science. Please send your CV and cover letter to Jonathan Tang to inquire about opportunities.
The chosen PDF is split into chunks, the page order of each chunk is rearranged, then the pages are placed two to a page in a new PDF. After printing and folding, each bundle of sheets should be a correctly ordered signature, ready for binding.
If you have a duplex printer, each signature will print in one go. If, like most people, you have a plain old one-side-at-time printer, each signature consists of two PDFs and you will have to print one, then turn the sheets over by hand to print the other. This can be a fraught operation, so make sure the pages are the correct way round and don't get jammed on the second trip through.
Maybe you have a non-PDF document you want to bind, say a Gutenberg Project plain text file or a Rich Text Format document. In this case, the easiest option is to load it into a word processor, such as OpenOffice, format it and use the 'Export as PDF' option to create a PDF. OpenOffice can also import MSWord documents.
Bookbinder is written in Jython, a version of Python that integrates pretty seamlessly with Java. The PDF grunt work is provided by Bruno Lowagie's marvellous itext, a PDF generating library, which is versatile and well thought of. The viewers are built using the JPedal library. The GUI is built using Swing, a standard Java GUI API, which seems to work well with Jython.
Modern small molecule protein modulators are developed using an array of drug discovery technologies such as fragment-based ligand discovery (FBLD), high-throughput screening (HTS), and DNA-encoded libraries (DELs). Library sizes and specific assays vary greatly, but irrespective of the preferred discovery technique, the ability to evaluate target engagement of every molecule in a screening library is essential to properly identifying novel binders and unlocking mechanistic details of newly developed drugs. Since screening libraries can be sizable collections of compounds, prodigious data sets are produced which require accurate and efficient methods of analysis to quickly identify hits. Academic labs are typically at a disadvantage when processing this data, as there are few methods that are both user-friendly and freely available. For users interested in such a method, this tutorial summarizes the widely popular thermal shift assay, discusses the existing approaches and workflows for thermal shift data processing, then outlines the development of a new, freely available, efficient cheminformatics workflow.
The thermal shift assay (TSA) has become a mainstream technique to evaluate ligand binding owing to its ease of execution and high rate of throughput. Proteins fold in a manner that minimizes free energy. Hydrophobic residues pack into the interior of a structure and hydrophilic residues are exposed on the exterior [1]. The structures are stabilized by intramolecular forces such as electronic van der Waals attractions, dipole interactions, and hydrogen bonds [2]. As thermal energy increases, these intramolecular protein forces are disrupted, leading to protein structure destabilization, denaturation, and the exposure of interior hydrophobic sites. Thermal denaturation can be monitored by fluorescence detection with real-time PCR instruments using intrinsic tryptophan fluorescence, or environmentally sensitive dyes, i.e., dyes which have increased quantum yield upon binding hydrophobic environments [3]. A graph of fluorescence intensity vs. temperature yields a characteristic melting curve, and the temperature of heat denaturation (Tm) is determined as the midpoint of the melting transition region. The Tm is influenced by sample conditions; bound ligands alter Tm values and therefore, experimentally significant ΔTm observances are generally indicative of binding (Fig. 1) [4].
An example melting curve demonstrating protein denaturation with increasing temperature. The inflection point of the melting curve, which corresponds to the midpoint of the protein denaturation process, is the melting temperature (Tm). Upon ligand binding, the stabilized protein exhibits a higher Tm
The data analysis process varies between laboratories, but often involves exporting the fluorescence data from the instrument, importing it to a separate software package to obtain melting temperatures, then exporting to another software package(s) for further analysis, hit determination, and visualization. The number of data transfers depends on the chosen form of Tm determination, available software, and the use of optional Microsoft Excel plug-ins. Some qPCR instrument manufacturers provide software for analysis of thermal shift data, to make this increasingly popular assay more user friendly, but these become less user friendly when analyzing data from multiple plates. Moreover, if the software use requires additional licensing, this introduces a potentially costly roadblock.
Workflow-based environments provide a common platform for multiple tools and have swiftly been adopted by the scientific community [12, 13]. The Konstanz Information Miner (KNIME) [14] is a free and open-source data analytics workflow platform which supports a wide range of functionality and has an active cheminformatics and bioinformatics community [15,16,17,18,19,20,21,22,23,24,25]. Its modular environment allows users to visually assemble and adapt the analysis flow from standardized building blocks called nodes, which are then connected through pipes carrying data or models. Nodes can be executed selectively and intermediate results checked via a graphical user interface. Metanodes and components can be used to encapsulate parts of a workflow and can even be re-used between workflows, allowing for the creation of complex but well-structured workflows. The capabilities of KNIME can be expanded by incorporating open-source third-party nodes including R integration, and open-source cheminformatics toolkits such as the RDKit, both of which are used in our workflow.
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