Studies of the Cardiolipin Interactome

Nanodisks comprised solely of the anionic glyerophospholipid cardiolipin and an apolipoprotein have been formulated and characterized. Cardiolipin nanodisks (CL ND) are composed of a planar lipid bilayer comprised solely of cardiolipin and an apolipoprotein scaffold, which circumscribes the unstable edge of the bilayer, conferring stability and complete aqueous solubility. These nanoparticles were used in a variety of experiments to better understand the interactions between cardiolipin and several members of its interactome. Calcium has previously been shown to induce a bilayer to non-bilayer transition when added to liposomes containing cardiolipin, but not in liposomes with a similar lipid composition in the absence of cardiolipin. This phenomenon was evaluated using ND technology. CL ND were found to be a homogenous population of completely aqueous soluble nanoparticles of approximately 200-300 kDa in size. CL ND undergo a bilayer to non-bilayer transformation following addition of sufficient CaCl2, similarly to liposomes and vesicles. This phenomenon is specific to divalent cations, as tested by addition of CaCl2, MgCl2, SrCl2. Addition of NaCl and KCl did not elicit a similar response. This phenomenon is also related to cardiolipin concentration as a component of the lipid bilayer, and decreasing the cardiolipin content results in increased resistance to calcium induced transformation. Ultimately, a novel molecular mechanism was put forth wherein cardiolipin molecules, upon addition of a divalent cation, reposition to allow the phosphate moieties to better bind. This repositioning increases the strain on the fatty acyl chains, forcing them to reposition and further increase the “cone” shape of the cardiolipin molecule. This interaction reduces the ability of the cardiolipin to reside stably in a planar bilayer. Once a sufficient number of molecules undergo this change, the constraining force on the nanodisk imposed by the apolipoprotein scaffold is exceeded, resulting in a transition to a non-bilayer state. CL ND were then used to investigate the interactions between cardiolipin, calcium and cytochrome c. Cytochrome c was found to stably bind to CL ND, resulting in co-elution following size exclusion chromatography. Pre-incubation of cytochrome c with CL ND increased susceptibility of CL ND to calcium induced bilayer to non-bilayer transformation, as compared to empty cardiolipin nanodisks. However, following this transformation, ~ 30% of the cytochrome c remained bound to the insoluble lipid-containing pellet. Addition of calcium insufficient to induce this transformation was found to lead to complete dissociation of cytochrome c. CL ND pre-incubated with a similar concentration of calcium no longer bind cytochrome c, suggesting competition for a specific binding site. This revelation may play an important role in furthering our understanding of cytochrome c’s release from the mitochondria during cell mediated apoptosis. Doxorubicin (DOX) is a clinically important anti-cancer drug. However, usage is restricted due to symptoms that have been termed “DOX induced cardiotoxicity”. This condition causes symptoms reminiscent, but distinct from, cardiomyopathy and often lead to heart failure. CL ND were investigated as a delivery vehicle to potentially reduce DOX induced cardiotoxicity. DOX CL ND were formulated and found to remain bound following dialysis. A novel assay was designed and tested to show that DOX, when given a choice between DNA or cardiolipin, will preferentially bind to DNA. Further studies showed that DOX CL ND retain a similar anti-cancer effect as free DOX in two different cancer cell lines while being more effective than liposomal DOX. Furthermore, it was found that DOX binding to CL ND increases cell viability as compared to free DOX in a rat cardiomyocyte cell line. Analysis of mitochondrial respiration of treated cells via XF24 Seahorse analysis suggests that binding of DOX to CL ND reduces mitochondrial dysfunction, as is evidenced by an increase in maximal respiration as compared to DOX treated samples. These data presented in this dissertation illustrates that CL ND are a useful tool to better understand mitochondrial interactions, an area that has proven difficult to study

Implementation of an Improved Image Enhancement Algorithm on FPGA

Image processing plays very crucial role in this digital human world and has rapidly evolved with the development of computers, mathematics and the real-life demand of variety of applications in wide range of areas. This wide range of areas includes remote sensing, machine/ robot vision, pattern recognition, medical diagnosis, video processing, military, agriculture, television, etc. Image processing has two important components which are image enhancement and information extraction. Since image enhancement works at the front end with the initial raw inputs, it works like a backbone in image processing. When it comes to implementing these image enhancement techniques and developing applications, these tasks are bit demanding in the choice of processing units because the demand of high resolution. This emerges the necessity of a high speed, powerful and cost-effective processing unit. In this thesis we present an improved image enhancement algorithm in terms of performance and its implementation on FPGA as they satiates the necessity of high speed, powerful and cost-effective processing unit by providing flexibility, parallelization, pipelining and reconfigurability. We have performed a high level synthesis by using MATLAB and implemented an improved image enhancement algorithm on Cyclone V by using Quartus Prime. We have considered an X-ray image size of 1000x1920p for implementation and achieved decent PSNR values and hardware resource utilization along with the better visual interpretability by our proposed improvements. For achieving a better execution time and power consumption we also offer the task parallelism for the algorithm

Implementation Of An Improved Image Enhancement Algorithm On FPGA

Image processing plays very crucial role in this digital human world and has rapidly evolved with the development of computers, mathematics and the real-life demand of variety of applications in wide range of areas. This wide range of areas includes remote sensing, machine/ robot vision, pattern recognition, medical diagnosis, video processing, military, agriculture, television, etc. Image processing has two important components which are image enhancement and information extraction. Since image enhancement works at the front end with the initial raw inputs, it works like a backbone in image processing. When it comes to implementing these image enhancement techniques and developing applications, these tasks are bit demanding in the choice of processing units because the demand of high resolution. This emerges the necessity of a high speed, powerful and cost-effective processing unit. In this thesis we present an improved image enhancement algorithm in terms of performance and its implementation on FPGA as they satiates the necessity of high speed, powerful and cost-effective processing unit by providing flexibility, parallelization, pipelining and reconfigurability. We have performed a high level synthesis by using MATLAB and implemented an improved image enhancement algorithm on Cyclone V by using Quartus Prime. We have considered an X-ray image size of 1000x1920p for implementation and achieved decent PSNR values and hardware resource utilization along with the better visual interpretability by our proposed improvements. For achieving a better execution time and power consumption we also offer the task parallelism for the algorithm

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Relationship between maturation, strength, movement competency and motor skill performance in adolescent males

Despite the limited research available, understanding how maturation, strength and movement skill influence long-term athletic development is crucial when working with young people. Therefore, the purpose of this study was to examine the relationships between maturation, strength, movement competency and motor skill performance in young males. One-hundred and ten adolescent males (mean ± SD; age 13.8 ± 0.6 y; height = 165.8 ± 9.4 cm; mass = 57.1 ± 13.9 kg; maturity offset = 0.1 ± 0.9 y) were tested for movement competency (resistance training skills battery, RTSB), strength (isometric midthigh pull, IMTP), speed (10, 20, 30 m sprint), power (horizontal jump, HJ; vertical jump, CMJ; seated medicine ball throw, SMBT) and repeat sprint ability (RSA). Results showed that maturity offset had small correlations with CMJ (r = 0.25), moderate correlations with speed (r = -0.31 to -0.35) and HJ (r = 0.33), and strong correlations with absolute strength (r = 0.70) and SMBT (r = 0.76). Relative strength showed small to large correlations with all motor skill variables (r = 0.27-0.61), whereas absolute strength was significantly correlated with speed, power and RSA (r = 0.29-0.83). The RTSB score showed small to moderate correlations with RSA (r = 0.27) and 20 and 30 m sprint performance (r = -0.34). Relative strength was the strongest predictor for all sprints (adjusted R2 = 0.38-0.40), CMJ (adjusted R2 = 0.16) and RSA (adjusted R2 = 0.27), whereas absolute strength was strongest for HJ and SMBT (adjusted R2 = 0.21 and 0.70, respectively). Maturity offset further explained sprint, CMJ and SMBT performance whereas RTSB did not help predict the performance of any dependent variables. Strength, movement competency and maturity are important considerations for motor skill performance, but strength may be most important and should be developed early on using appropriate training recommendations

Design Techniques for Energy-Quality Scalable Digital Systems

Energy efficiency is one of the key design goals in modern computing. Increasingly complex tasks are being executed in mobile devices and Internet of Things end-nodes, which are expected to operate for long time intervals, in the orders of months or years, with the limited energy budgets provided by small form-factor batteries. Fortunately, many of such tasks are error resilient, meaning that they can toler- ate some relaxation in the accuracy, precision or reliability of internal operations, without a significant impact on the overall output quality. The error resilience of an application may derive from a number of factors. The processing of analog sensor inputs measuring quantities from the physical world may not always require maximum precision, as the amount of information that can be extracted is limited by the presence of external noise. Outputs destined for human consumption may also contain small or occasional errors, thanks to the limited capabilities of our vision and hearing systems. Finally, some computational patterns commonly found in domains such as statistics, machine learning and operational research, naturally tend to reduce or eliminate errors. Energy-Quality (EQ) scalable digital systems systematically trade off the quality of computations with energy efficiency, by relaxing the precision, the accuracy, or the reliability of internal software and hardware components in exchange for energy reductions. This design paradigm is believed to offer one of the most promising solutions to the impelling need for low-energy computing. Despite these high expectations, the current state-of-the-art in EQ scalable design suffers from important shortcomings. First, the great majority of techniques proposed in literature focus only on processing hardware and software components. Nonetheless, for many real devices, processing contributes only to a small portion of the total energy consumption, which is dominated by other components (e.g. I/O, memory or data transfers). Second, in order to fulfill its promises and become diffused in commercial devices, EQ scalable design needs to achieve industrial level maturity. This involves moving from purely academic research based on high-level models and theoretical assumptions to engineered flows compatible with existing industry standards. Third, the time-varying nature of error tolerance, both among different applications and within a single task, should become more central in the proposed design methods. This involves designing “dynamic” systems in which the precision or reliability of operations (and consequently their energy consumption) can be dynamically tuned at runtime, rather than “static” solutions, in which the output quality is fixed at design-time. This thesis introduces several new EQ scalable design techniques for digital systems that take the previous observations into account. Besides processing, the proposed methods apply the principles of EQ scalable design also to interconnects and peripherals, which are often relevant contributors to the total energy in sensor nodes and mobile systems respectively. Regardless of the target component, the presented techniques pay special attention to the accurate evaluation of benefits and overheads deriving from EQ scalability, using industrial-level models, and on the integration with existing standard tools and protocols. Moreover, all the works presented in this thesis allow the dynamic reconfiguration of output quality and energy consumption. More specifically, the contribution of this thesis is divided in three parts. In a first body of work, the design of EQ scalable modules for processing hardware data paths is considered. Three design flows are presented, targeting different technologies and exploiting different ways to achieve EQ scalability, i.e. timing-induced errors and precision reduction. These works are inspired by previous approaches from the literature, namely Reduced-Precision Redundancy and Dynamic Accuracy Scaling, which are re-thought to make them compatible with standard Electronic Design Automation (EDA) tools and flows, providing solutions to overcome their main limitations. The second part of the thesis investigates the application of EQ scalable design to serial interconnects, which are the de facto standard for data exchanges between processing hardware and sensors. In this context, two novel bus encodings are proposed, called Approximate Differential Encoding and Serial-T0, that exploit the statistical characteristics of data produced by sensors to reduce the energy consumption on the bus at the cost of controlled data approximations. The two techniques achieve different results for data of different origins, but share the common features of allowing runtime reconfiguration of the allowed error and being compatible with standard serial bus protocols. Finally, the last part of the manuscript is devoted to the application of EQ scalable design principles to displays, which are often among the most energy- hungry components in mobile systems. The two proposals in this context leverage the emissive nature of Organic Light-Emitting Diode (OLED) displays to save energy by altering the displayed image, thus inducing an output quality reduction that depends on the amount of such alteration. The first technique implements an image-adaptive form of brightness scaling, whose outputs are optimized in terms of balance between power consumption and similarity with the input. The second approach achieves concurrent power reduction and image enhancement, by means of an adaptive polynomial transformation. Both solutions focus on minimizing the overheads associated with a real-time implementation of the transformations in software or hardware, so that these do not offset the savings in the display. For each of these three topics, results show that the aforementioned goal of building EQ scalable systems compatible with existing best practices and mature for being integrated in commercial devices can be effectively achieved. Moreover, they also show that very simple and similar principles can be applied to design EQ scalable versions of different system components (processing, peripherals and I/O), and to equip these components with knobs for the runtime reconfiguration of the energy versus quality tradeoff

Protein traffic in parasites and mammalian cells. Proceedings of a workshop

The International Laboratory for Research on Animal Diseases (ILRAD) has recently developed interests in several areas of cell biology and biochemistry that are relevant to trypanosomiasis and theileriosis (East Coast Fever (ECF)). The reasons for this interest stem from a perceived difficulty of tackling trypanosomiasis by conventional vaccination procedures due to the tremendous antigenic variation that trypanosomes can undergo. Thus approaches other than a purely immunological one are now being explored. As a part of this new emphasis, this workshop was held to review the current state of knowledge in the field of protein traffic and catabolism and to attempt to address these issues, with particular reference to parasitic diseases. The topics covered include such areas as protein targeting, protein assembly, protein degradation, endocytosis and lysosomal activity

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence