Arginase is a widely known enzyme of the urea cycle that catalyzes the hydrolysis of L-arginine to L-ornithine and urea. The action of arginase goes beyond the boundaries of hepatic ureogenic ...function, being widespread through most tissues. Two arginase isoforms coexist, the type I (Arg1) predominantly expressed in the liver and the type II (Arg2) expressed throughout extrahepatic tissues. By producing L-ornithine while competing with nitric oxide synthase (NOS) for the same substrate (L-arginine), arginase can influence the endogenous levels of polyamines, proline, and NO
. Several pathophysiological processes may deregulate arginase/NOS balance, disturbing the homeostasis and functionality of the organism. Upregulated arginase expression is associated with several pathological processes that can range from cardiovascular, immune-mediated, and tumorigenic conditions to neurodegenerative disorders. Thus, arginase is a potential biomarker of disease progression and severity and has recently been the subject of research studies regarding the therapeutic efficacy of arginase inhibitors. This review gives a comprehensive overview of the pathophysiological role of arginase and the current state of development of arginase inhibitors, discussing the potential of arginase as a molecular imaging biomarker and stimulating the development of novel specific and high-affinity arginase imaging probes.
Struck: Structured Output Tracking with Kernels Hare, Sam; Golodetz, Stuart; Saffari, Amir ...
IEEE transactions on pattern analysis and machine intelligence,
2016-Oct.-1, 2016-10-00, 2016-10-1, 20161001, Letnik:
38, Številka:
10
Journal Article
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Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online ...learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we avoid the need for an intermediate classification step. Our method uses a kernelised structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow our tracker to run at high frame rates, we (a) introduce a budgeting mechanism that prevents the unbounded growth in the number of support vectors that would otherwise occur during tracking, and (b) show how to implement tracking on the GPU. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased tracking performance.
Neuromorphic computing is an emerging computing paradigm beyond the conventional digital von Neumann computation. An oxide‐based resistive switching memory is engineered to emulate synaptic devices. ...At the device level, the gradual resistance modulation is characterized by hundreds of identical pulses, achieving a low energy consumption of less than 1 pJ per spike. Furthermore, a stochastic compact model is developed to quantify the device switching dynamics and variation. At system level, the performance of an artificial visual system on the image orientation or edge detection with 16 348 oxide‐based synaptic devices is simulated, successfully demonstrating a key feature of neuromorphic computing: tolerance to device variation.
A dynamic Verilog-A resistive random access memory (RRAM) compact model, including cycle-to-cycle variation, is developed for circuit/system explorations. The model not only captures dc and ac ...behavior, but also includes intrinsic random fluctuations and variations. A methodology to systematically calibrate the model parameters with experiments is presented and illustrated with a broad set of experimental data, including multilayer RRAM. The physical meanings of the various model parameters are discussed. An example of applying the RRAM cell model to a ternary content-addressable-memory (TCAM) macro is provided. Tradeoffs on the design of RRAM devices for the TCAM macro are discussed in the context of the energy consumption and worst case latency of the memory array.
The emerging paradigm of 'abundant-data' computing requires real-time analytics on enormous quantities of data collected by a mushrooming network of sensors. Todays computing technology, however, ...cannot scale to satisfy such big data applications with the required throughput and energy efficiency. The next technology frontier will be monolithically integrated chips with three-dimensionally interleaved memory and logic for unprecedented data bandwidth with reduced energy consumption. In this work, we exploit the atomically thin nature of the graphene edge to assemble a resistive memory (∼ 3 Å thick) stacked in a vertical three-dimensional structure. We report some of the lowest power and energy consumption among the emerging non-volatile memories due to an extremely thin electrode with unique properties, low programming voltages, and low current. Circuit analysis of the three-dimensional architecture using experimentally measured device properties show higher storage potential for graphene devices compared that of metal based devices.
Focal cortical dysplasias (FCDs) are malformations of cortical development (MCDs) that are highly associated with medication-resistant epilepsy and are the most common cause of neocortical epilepsy ...in children. FCDs are a heterogeneous group of developmental disorders caused by germline or somatic mutations that occur in genes regulating the PI3K Akt mTOR pathway-a key pathway in neuronal growth and migration. Accordingly, FCDs are characterized by abnormal cortical lamination, cell morphology (e.g., cytomegaly), and cellular polarity. In some FCD subtypes, balloon cells express proteins typically seen in neuroglial progenitor cells. Because recurrent intractable seizures are a common feature of FCDs, epileptogenic electrophysiological properties are also observed in addition to local inflammation. Here, we will summarize the current literature regarding FCDs, addressing the current classification system, histopathology, molecular genetics, electrophysiology, and transcriptome and cell signaling changes.
Brain malignancies encompass a range of primary and metastatic cancers, including low-grade and high-grade gliomas and brain metastases (BrMs) originating from diverse extracranial tumors. Our ...understanding of the brain tumor microenvironment (TME) remains limited, and it is unknown whether it is sculpted differentially by primary versus metastatic disease. We therefore comprehensively analyzed the brain TME landscape via flow cytometry, RNA sequencing, protein arrays, culture assays, and spatial tissue characterization. This revealed disease-specific enrichment of immune cells with pronounced differences in proportional abundance of tissue-resident microglia, infiltrating monocyte-derived macrophages, neutrophils, and T cells. These integrated analyses also uncovered multifaceted immune cell activation within brain malignancies entailing converging transcriptional trajectories while maintaining disease- and cell-type-specific programs. Given the interest in developing TME-targeted therapies for brain malignancies, this comprehensive resource of the immune landscape offers insights into possible strategies to overcome tumor-supporting TME properties and instead harness the TME to fight cancer.
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•Flow cytometry, RNA-seq, and protein and image analyses reveal brain TME complexity•Glioma IDH mutation status and brain metastasis primary tumors shape the brain TME•Microglia and monocyte-derived macrophages exhibit multifaceted activation•TME immune cells show disease- and cell-type-specific expression patterns
High-dimensional, multi-omics characterization of the brain tumor microenvironment, including comparisons of gliomas and brain metastases, suggests that education of immune cell types in the TME depends on tumor origin and IDH mutational status.
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