Structural mass spectrometry approaches to understand multidrug efflux systems

Multidrug efflux pumps are ubiquitous across both eukaryotes and prokaryotes, and have major implications in antimicrobial and multidrug resistance. They reside within cellular membranes and have proven difficult to study owing to their hydrophobic character and relationship with their compositionally complex lipid environment. Advances in structural mass spectrometry (MS) techniques have made it possible to study these systems to elucidate critical information on their structure–function relationships. For example, MS techniques can report on protein structural dynamics, stoichiometry, connectivity, solvent accessibility, and binding interactions with ligands, lipids, and other proteins. This information proving powerful when used in conjunction with complementary structural biology methods and molecular dynamics (MD) simulations. In the present review, aimed at those not experts in MS techniques, we report on the current uses of MS in studying multidrug efflux systems, practical considerations to consider, and the future direction of the field. In the first section, we highlight the importance of studying multidrug efflux proteins, and introduce a range of different MS techniques and explain what information they yield. In the second section, we review recent studies that have utilised MS techniques to study and characterise a range of different multidrug efflux systems

The Role of Hypernetworks as a Multilevel Methodology for Modelling and Understanding Dynamics of Team Sports Performance.

Despite its importance in many academic fields, traditional scientific methodologies struggle to cope with analysis of interactions in many complex adaptive systems, including team sports. Inherent features of such systems (e.g. emergent behaviours) require a more holistic approach to measurement and analysis for understanding system properties. Complexity sciences encompass a holistic approach to research on collective adaptive systems, which integrates concepts and tools from other theories and methods (e.g. ecological dynamics and social network analysis) to explain functioning of such systems in their natural environments. Multilevel networks and hypernetworks comprise novel and potent methodological tools for assessing team dynamics at more sophisticated levels of analysis, increasing their potential to impact on competitive performance in team sports. Here, we discuss how concepts and tools derived from studies of multilevel networks and hypernetworks have the potential for revealing key properties of sports teams as complex, adaptive social systems. This type of analysis can provide valuable information on team performance, which can be used by coaches, sport scientists and performance analysts for enhancing practice and training. We examine the relevance of network sciences, as a sub-discipline of complexity sciences, for studying the dynamics of relational structures of sports teams during practice and competition. Specifically, we explore the benefits of implementing multilevel networks, in contrast to traditional network techniques, highlighting future research possibilities. We conclude by recommending methods for enhancing the applicability of hypernetworks in analysing team dynamics at multiple levels

Statistical physics and information theory perspectives on complex systems and networks

Complex physical, biological, and sociotehnical systems often display various phenomena that can't be understood using traditional tools of single disciplines. We describe work on developing and applying theoretical methods to understand phenomena of this type, using statistical physics, networks, spectral graph theory, information theory, and geometry. Financial systems--being highly stochastic, with agents in a complex environment--offer a unique arena to develop and test new ways of thinking about complexity. We develop a framework for analyzing market dynamics motivated by linear response theory, and propose a model based on agent behavior that naturally incorporates external influences. We investigate central issues such as price dynamics, processing and incorporation of information, and how agent behavior influences stability. We find that the mean field behavior of our model captures important aspects of return dynamics, and identify a stable-unstable regime transition depending on easily measurable model parameters. Our methods naturally connect external factors to internal market features and behaviors, and therefore address the crucial question of how system stability relates to agent behavior and external forces. Complex systems are often interconnected heterogeneously, with subunits influencing others counterintuitively due to specific details of their connections. Correlations are insufficient to characterize this due to, e.g., being symmetric and unable to discern directional relationships. We synthesize ideas from information and network theory to introduce a general tool for studying such relations in networks. Based on transfer entropy, we propose a measure--Effective Transfer Entropy Dependency--that measures influence by considering precisely how much of a source node's influence on targets is due to intermediates. We apply this to indices of the world's major markets, finding that our measure anticipates same-day correlation structure from lagged time-series data, and identifies influencers not found using standard correlations. Graphs are essential for understanding complex systems and datasets. We present new methods for identifying important structure in graphs, based on ideas from quantum information theory and statistical mechanics, and the renormalization group. We apply information geometry and spectral geometry to study the geometric structures that arise from graphs and random graph models, and suggest future extensions and applications to important problems like graph partitioning and machine learning.2020-04-22T00:00:00

Integrated Sensing and Communications: Recent Advances and Ten Open Challenges

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S$\&$C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals.Comment: 26 pages, 22 figures, resubmitted to IEEE Journal. Appreciation for the outstanding contributions of coauthors in the paper

Model-Based Deep Learning

Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. On the other hand, purely data-driven approaches that are model-agnostic are becoming increasingly popular as datasets become abundant and the power of modern deep learning pipelines increases. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance, especially for supervised problems. However, DNNs typically require massive amounts of data and immense computational resources, limiting their applicability for some signal processing scenarios. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches. Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, as well as learning from limited data. In this article we survey the leading approaches for studying and designing model-based deep learning systems. We divide hybrid model-based/data-driven systems into categories based on their inference mechanism. We provide a comprehensive review of the leading approaches for combining model-based algorithms with deep learning in a systematic manner, along with concrete guidelines and detailed signal processing oriented examples from recent literature. Our aim is to facilitate the design and study of future systems on the intersection of signal processing and machine learning that incorporate the advantages of both domains

Development of a calibration procedure for integration of dual fluoroscopy and motion analysis

INTRODUCTION Accurate quantification of in vivo effects of injury on joint mechanics is essential to identify movement abnormality and related joint pathologies such as osteoarthritis. Typically used Motion Analysis (MA) technologies for studying human gait and injury suffer from soft tissue movement artifact, which may prohibit identification of small but significant changes of joint motion. High-speed dual fluoroscopy (DF) systems such as the one at the Clinical Movement Assessment Laboratory, University of Calgary, provide movement-artifact-free, high-resolution (0.30-0.44°, 0.25-0.33mm) [1], in vivo bone kinematics during dynamic activities. Such systems however, represent a trade-off between high system accuracy and limited field of view (FOV~10 inch) [2] compared to MA systems. DF systems therefore typically provide information only for a single joint while MA systems may capture the whole body. This project worked toward the integration of traditional MA and state-of-the-art DF systems to provide high accuracy joint as well as lower limb kinematics. The aim was to create hardware and software solutions for the calibration of a DF system for integration with MA systems. METHODS A Plexiglas calibration frame (48” x 22”) with an integrated steel bead grid (95 x 41, 0.125″ diameter) was designed and built. The calibration frame pattern spanned the entire frame to allow easy identification of the pattern in the small FOV of the X-ray images. A unique braille design with letters for each row and column was implemented to support simple bead location identification and future automated procedures. Three sets of column identifiers were placed at the left, center, and right regions. This pattern was glued into the calibration frame using 0.125" diameter spherical steel beads. The DF 3D coordinates were determined by imaging a custom calibration cube and using a modified direct linear transform [3]. The calibration frame was placed on top of the treadmill and images were acquired by the DF system. A MATLAB program was developed to process the calibration frame images. A Hough Transform-based circle detection function was used for digitizing the beads in both images. The user then identified the bead ID’s in the X-ray images. Combining the X-ray image bead locations, the DF 3D coordinate system, and the calibration frame’s physical parameters, the planar equation for the treadmill location can be computed. RESULTS Figure 1 shows an image of the calibration device positioned on the treadmill, as well as the resulting X-ray images. The braille pattern was successful in allowing the user to identify the pattern and its beads. DISCUSSION AND CONCLUSIONS The calibration frame developed in provides information of the spatial location of the instrumented treadmill. This is instrumental for integrating the DF and MA systems. Without this calibration device, all joint movements are observed as floating in 3D space and information about the joint’s interaction with the ground is not accessible. Further, without systems integration, no knowledge is available for the interaction of multiple joints of the lower limbs, which contains critical information for biomechanical investigations of injury and disease. Future developments based on these methods will provide the planar equations of the treadmill to provide full systems integration.

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Clonal structures and cell interactions in cancer

Despite sharing an identical genome, cells of higher order multicellular organisms display a large degree of phenotypic diversity. This diversity is maintained by a sophisticated regulatory machinery that integrates information from both intrinsic and extrinsic factors, ultimately coordinating the appropriate gene expression. Sequencing methods such as RNA and DNA sequencing have become indispensable tools in the pursuit to understand gene regulation. In recent years, the integration of single-cell sequencing techniques and CRISPR-based methods has ushered in a new era of genomic exploration, providing unprecedented opportunities to investigate the intricate interplay between genes, cellular processes, and disease progression. These cutting-edge advances have transformed the research landscape, enabling in-depth studies of gene regulation in single cells, and paving the way for future discoveries in both healthy and malignant tissues. While cancer has traditionally been studied as a genetic disease, it is now evident that mutations alone do not determine cancer initiation or progression. This notion is supported by two key observations: first, cancer-driving mutations do not always lead to malignancy; and second, identical mutations can yield different outcomes depending on the cell type in which they occur. Consequently, a deeper understanding of gene regulation and the various ways it is modulated is critical for deciphering the complex relationship between genetic changes and cancer initiation. In this thesis we aimed to develop novel single-cell methodologies applicable to studying biological complex systems. We have developed four techniques: CIM-seq, DNTR-seq, Smart3-ATAC, and ACTIseq, described in papers I-IV, respectively. The methods all capture additional modalities in combination with single-cell RNA-seq data, including spatial information, whole genome sequencing, accessible chromatin, and direct read out of guide RNAs. We applied these methods to investigate biological systems at the single-cell level, offering a more comprehensive understanding of cellular behavior in health and disease. Our approaches have allowed us to characterize stem cell niches and regeneration dynamics in the epithelial layer of the colon, and delve into the effects of gene dosage, quantifying how mutational changes impact transcriptional output. Furthermore, we have explored the complex landscape of gene regulation within pancreatic ductal adenocarcinomas, identifying mechanisms that enable cancer growth and proliferation. This body of work emphasizes the importance of multimodal and integrative approaches for unraveling the complexities of biological systems at a cellular level. The methods we've developed represent a significant step forward, promising to facilitate the discovery of molecular targets for cancer therapeutics