Cosmic Dust Collection Facility: Scientific objectives and programmatic relations

The science objectives are summarized for the Cosmic Dust Collection Facility (CDCF) on Space Station Freedom and these objectives are related to ongoing science programs and mission planning within NASA. The purpose is to illustrate the potential of the CDCF project within the broad context of early solar system sciences that emphasize the study of primitive objects in state-of-the-art analytical and experimental laboratories on Earth. Current knowledge about the sources of cosmic dust and their associated orbital dynamics is examined, and the results are reviewed of modern microanalytical investigations of extraterrestrial dust particles collected on Earth. Major areas of scientific inquiry and uncertainty are identified and it is shown how CDCF will contribute to their solution. General facility and instrument concepts that need to be pursued are introduced, and the major development tasks that are needed to attain the scientific objectives of the CDCF project are identified

Knowledge Brokering and Organizational Innovation: Founder Imprinting Effects

We empirically examine the innovation consequences of organizational knowledge brokering, the ability to effectively apply knowledge from one technical domain to innovate in another. We investigate how organizational innovation outcomes vary by founders’ initial mode of venture ideation. We then compare how firms established with knowledge-brokering-based ideation differ in their methods of sustaining ongoing knowledge-brokering capacity compared with firms not established in such a manner. We do so by tracking all the start-up biotechnology firms founded to commercialize the then-emergent recombinant DNA technology (the sample of initial knowledge brokers) together with a contemporaneously founded sample of biotechnology firms that did not license the DNA technology (the sample of initial nonbrokers). Our results suggest that (a) ongoing knowledge brokering has an inverted U-shaped relationship with innovative performance in general; (b) initial knowledge brokers have a positive imprinting effect on their organizations’ search patterns over time, resulting in superior performance relative to nonbrokers; and (c) initial nonbrokers rely more on external channels of sourcing knowledge, such as hiring technical staff, relative to initial brokers, reinforcing the imprinting interpretation. The described imprinting mechanism differs from extant mechanisms such as partner affiliation- and trigger-based mechanisms in explaining entrepreneurial performance differentials

Combined supervised and unsupervised learning to identify subclasses of disease for better prediction

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDisease subtyping, which aids in the development of personalised treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if I can identify subclasses of disease, this will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. In addition, patients might suffer from multiple disease complications. Models that are tailored to individuals could improve both prediction of multiple complications and understanding of underlying disease characteristics. However, AI models can become outdated over time due to either sudden changes in the underlying data, such as those caused by new measurement methods, or incremental changes, such as the ageing of the study population. This thesis proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The method was tested on a freely available dataset of real-world breast cancer cases and data from a London hospital on systemic sclerosis, a rare and potentially fatal condition. The results show that nearest consensus clustering classification improves accuracy and prediction significantly when this algorithm is compared with competitive similar methods. In addition, this thesis proposes a new algorithm that integrates latent class models with classification. The new algorithm uses latent class models to cluster patients within groups; this results in improved classification and aids in the understanding of the underlying differences of the discovered groups. The method was tested on data from patients with systemic sclerosis (SSc), a rare and potentially fatal condition, and coronary heart disease. Results show that the latent class multi-label classification (MLC) model improves accuracy when compared with competitive similar methods. Finally, this thesis implemented the updated concept drift method (DDM) to monitor AI models over time and detect drifts when they occur. The method was tested on data from patients with SSc and patients with coronavirus disease (COVID)

Structure, Genetics and Worldwide Spread of New Delhi Metallo-β-lactamase (NDM): a threat to public health

Background: The emergence of carbapenemase producing bacteria, especially New Delhi metallo-β-lactamase (NDM-1) and its variants, worldwide, has raised amajor public health concern. NDM-1 hydrolyzes a wide range of β-lactam antibiotics, including carbapenems, which are the last resort of antibiotics for the treatment of infections caused by resistant strain of bacteria. Main body: In this review, we have discussed blaNDM-1variants, its genetic analysis including type of specific mutation, origin of country and spread among several type of bacterial species. Wide members of enterobacteriaceae, most commonly Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, and gram-negative non-fermenters Pseudomonas spp. and Acinetobacter baumannii were found to carry these markers. Moreover, at least seventeen variants of blaNDM-type gene differing into one or two residues of amino acids at distinct positions have been reported so far among different species of bacteria from different countries. The genetic and structural studies of these variants are important to understand the mechanism of antibiotic hydrolysis as well as to design new molecules with inhibitory activity against antibiotics. Conclusion: This review provides a comprehensive view of structural differences among NDM-1 variants, which are a driving force behind their spread across the globe

Data and Computation Efficient Meta-Learning

In order to make predictions with high accuracy, conventional deep learning systems require large training datasets consisting of thousands or millions of examples and long training times measured in hours or days, consuming high levels of electricity with a negative impact on our environment. It is desirable to have have machine learning systems that can emulate human behavior such that they can quickly learn new concepts from only a few examples. This is especially true if we need to quickly customize or personalize machine learning models to specific scenarios where it would be impractical to acquire a large amount of training data and where a mobile device is the means for computation. We define a data efficient machine learning system to be one that can learn a new concept from only a few examples (or shots) and a computation efficient machine learning system to be one that can learn a new concept rapidly without retraining on an everyday computing device such as a smart phone. In this work, we design, develop, analyze, and extend the theory of machine learning systems that are both data efficient and computation efficient. We present systems that are trained using multiple tasks such that it "learns how to learn" to solve new tasks from only a few examples. These systems can efficiently solve new, unseen tasks drawn from a broad range of data distributions, in both the low and high data regimes, without the need for costly retraining. Adapting to a new task requires only a forward pass of the example task data through the trained network making the learning of new tasks possible on mobile devices. In particular, we focus on few-shot image classification systems, i.e. machine learning systems that can distinguish between numerous classes of objects depicted in digital images given only a few examples of each class of object to learn from. To accomplish this, we first develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. We then introduce Versa, an instance of the framework employing a fast, flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of training examples, and outputs a distribution over task-specific parameters in a single forward pass of the network. We evaluate Versa on benchmark datasets, where at the time, the method achieved state-of-the-art results when compared to meta-learning approaches using similar training regimes and feature extractor capacity. Next, we build on Versa and add a second amortized network to adapt key parameters in the feature extractor to the current task. To accomplish this, we introduce CNAPs, a conditional neural process based approach to multi-task classification. We demonstrate that, at the time, CNAPs achieved state-of-the-art results on the challenging Meta-Dataset benchmark indicating high-quality transfer-learning. Timing experiments reveal that CNAPs is computationally efficient when adapting to an unseen task as it does not involve gradient back propagation computations. We show that trained models are immediately deployable to continual learning and active learning where they can outperform existing approaches that do not leverage transfer learning. Finally, we investigate the effects of different methods of batch normalization on meta-learning systems. Batch normalization has become an essential component of deep learning systems as it significantly accelerates the training of neural networks by allowing the use of higher learning rates and decreasing the sensitivity to network initialization. We show that the hierarchical nature of the meta-learning setting presents several challenges that can render conventional batch normalization ineffective. We evaluate a range of approaches to batch normalization for few-shot learning scenarios, and develop a novel approach that we call TaskNorm. Experiments demonstrate that the choice of batch normalization has a dramatic effect on both classification accuracy and training time for both gradient based- and gradient-free meta-learning approaches and that TaskNorm consistently improves performance

Posttranslational Modifications and the Immunogenicity of Biotherapeutics

Whilst the amino acid sequence of a protein is determined by its gene sequence, the final structure and function are determined by posttranslational modifications (PTMs), including quality control (QC) in the endoplasmic reticulum (ER) and during passage through the Golgi apparatus. These processes are species and cell specific and challenge the biopharmaceutical industry when developing a production platform for the generation of recombinant biologic therapeutics. Proteins and glycoproteins are also subject to chemical modifications (CMs) both in vivo and in vitro. The individual is naturally tolerant to molecular forms of self-molecules but nonself variants can provoke an immune response with the generation of anti-drug antibodies (ADA); aggregated forms can exhibit enhanced immunogenicity and QC procedures are developed to avoid or remove them. Monoclonal antibody therapeutics (mAbs) are a special case because their purpose is to bind the target, with the formation of immune complexes (ICs), a particular form of aggregate. Such ICs may be removed by phagocytic cells that have antigen presenting capacity. These considerations may frustrate the possibility of ameliorating the immunogenicity of mAbs by rigorous exclusion of aggregates from drug product. Alternate strategies for inducing immunosuppression or tolerance are discussed

Examination of the immunoglobulin repertoire before and after Anthrax Vaccine Adsorbed immunization

Anthrax Vaccine Adsorbed (AVA) immunization protects against anthrax disease by eliciting a neutralizing antibody response. However, antigen-specific antibody concentrations are not observed in high quantities until three immunizations have been administered over six months. Even then, humoral responses to AVA do not provide long-term immunity without an annual booster. We followed six healthy volunteers over the five-dose, 18-month AVA schedule to characterize the genetics of the immunoglobulin repertoire during the vaccination series. Two tiers of data were collected: 1) Immunoglobulin variable region genes (IgVRG) from bulk sorted naïve, memory and plasmablast (PB) B cells and 2) single cell sorted and sequenced IgVRG from plasmablasts. Samples were collected prior to and one and two weeks following each immunization. Our initial analyses indicated that technical error, the variation introduced by biological sampling and standard sample preparation, resulted in skewed output, and we developed a model to better estimate quantitative values from Ig-seq. We also utilized unique molecular identifiers to correct for nucleotide errors and PCR over-amplification. Our analysis of IgVRG following AVA administration reveals that the population of peripheral PBs following primary immunization is not distinguishable from the pre-immune peripheral PB repertoire. These PBs have more somatic mutations than expected for newly activated and differentiated naïve B cells, and are unlikely to be vaccine-elicited. In contrast, PBs observed following the 2nd dose have low mutation frequencies that increase upon subsequent vaccination. These clones are more persistent than clones first observed following any other immunization, but still make up a very small proportion of the overall repertoire. At no time is the clonal repertoire consistently dominated by a few clones, and the total and plasmablast repertoires are highly transient, even after the elicitation of vaccine-specific antibodies. AVA immunization thus results in a polyclonal B cell response which is not dominated by one or a few highly specific, strongly-elicited clones. We conclude that primary immunization by AVA is not sufficiently immunogenic to elicit vaccine-responsive, class-switched PBs to the periphery, nor is complete AVA immunization able to sustain proliferation of individual clones, providing insight into why AVA may require regular boosts