3,023 research outputs found

    The effect of autologous macrophage therapy in cirrhosis in response to individual immune reparative pathways: developing a novel therapy

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    BACKGROUND: Liver cirrhosis is the end stage of any injury process to the liver. Once established it inevitably progresses to complications such as portal hypertension, cancer and death. There is not cure for liver cirrhosis besides liver transplant. We face an unmet demand for treatment of this condition. The role of macrophages in fibrosis development and resolution in the liver has been extensively investigated. Prof Forbes group invested in the development of autologous macrophage product to promote fibrosis resolution hence cirrhosis regression. This has demonstrated its efficacy and safety in animal models. From these encouraging pre-clinic data a phase 1 first in human clinical trial of autologous activated macrophage product for cirrhotic patients was developed. METHODS: Using an established 3+3 dose escalation model we enrolled a total of 9 subject in the phase 1 trial reaching a maximum achieved and safe dose of 1x10^9 macrophages. In addition to adverse events, dose toxicity and macrophage activation syndrome (MAS) parameter, we evaluated a varied range of circulating cytokines and chemokine pre and post treatment using a commercial kit. Moreover we developed a protocol for P13- magnetic resonance spectrometry (MRS) for the analysis of the metabolically active liver parenchyma. Data from the phase 1 trial were used to improve the autologous cellular produce and phase 2 randomised controlled trial. RESULTS: The autologous activated macrophage produce is demonstrated not to cause any toxicity in this first in human study of cirrhotic population of different aetiology. Cytokine and chemokine analysis supports these findings and specifically demonstrates low levels of IL-8, which represent cardinal feature of MAS. Other interesting cytokine signals may support extra cellular matrix remodelling effect of the autologous macrophage product infusion. In addition we demonstrated a reproducible protocol for MRS in liver disease. DISCUSSION: Autologous activated macrophage infusion did not result in any toxicity in cirrhotic subjects taking part in this study and shows preliminary signs of efficacy in fibrosis resolution both clinically and biochemically. This work places the basis of development of cellular products for treatment of cirrhosis and fibrosis and provides invaluable insight in immune response to cellular treatment

    Development of variable and robust brain wiring patterns in the fly visual system

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    Precise generation of synapse-specific neuronal connections are crucial for establishing a robust and functional brain. Neuronal wiring patterns emerge from proper spatiotemporal regulation of axon branching and synapse formation during development. Several neuropsychiatric and neurodevelopmental disorders exhibit defects in neuronal wiring owing to synapse loss and/or dys-regulated axon branching. Despite decades of research, how the two inter-dependent cellular processes: axon branching and synaptogenesis are coupled locally in the presynaptic arborizations is still unclear. In my doctoral work, I investigated the possible role of EGF receptor (EGFR) activity in coregulating axon branching and synapse formation in a spatiotemporally restricted fashion, locally in the medulla innervating Dorsal Cluster Neuron (M- DCN)/LC14 axon terminals. In this work I have explored how genetically encoded EGFR randomly recycles in the axon branch terminals, thus creating an asymmetric, non-deterministic distribution pattern. Asymmetric EGFR activity in the branches acts as a permissive signal for axon branch pruning. I observed that the M-DCN branches which stochastically becomes EGFR ‘+’ during development are synaptogenic, which means they can recruit synaptic machineries like Syd1 and Bruchpilot (Brp). My work showed that EGFR activity has a dual role in establishing proper M-DCN wiring; first in regulating primary branch consolidation possibly via actin regulation prior to synaptogenesis. Later in maintaining/protecting the levels of late Active Zone (AZ) protein Brp in the presynaptic branches by suppressing basal autophagy level during synaptogenesis. When M-DCNs lack optimal EGFR activity, the basal autophagy level increases resulting in loss of Brp marked synapses which is causal to increased exploratory branches and post-synaptic target loss. Lack of EGFR activity affects the M-DCN wiring pattern that makes adult flies more active and behave like obsessive compulsive in object fixation assay. In the second part of my doctoral work, I have asked how non-genetic factors like developmental temperature affects adult brain wiring. To test that, I increased or decreased rearing temperature which is known to inversely affect pupal developmental rate. We asked if all the noisy cellular processes of neuronal assembly: filopodial dynamics, axon branching, synapse formation and postsynaptic connections scale up or down accordingly. I observed that indeed all the cellular processes slow down at lower developmental temperature and vice versa, which changes the DCN wiring pattern accordingly. Interestingly, behavior of flies adapts to their developmental temperature, performing best at the temperature they have been raised at. This shows that optimal brain function is an adaptation of robust brain wiring patterns which are specified by noisy developmental processes. In conclusion, my doctoral work helps us better understand the developmental regulation of axon branching and synapse formation for establishing precise brain wiring pattern. We need all the cell intrinsic developmental processes to be highly regulated in space and time. It is infact a combinatorial effect of such stochastic processes and external factors that contribute to the final outcome, a functional and robust adult brain

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Supporting Safety Analysis of Deep Neural Networks with Automated Debugging and Repair

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    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    AB-INITIO INVESTIGATION OF 2D MATERIALS FOR GAS SENSING, ENERGY STORAGE AND SPINTRONIC APPLICATIONS

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    The field of Two Dimensional (2D) materials has been extensively studied since their discovery in 2004, owing to their remarkable combination of properties. My thesis focuses on exploring novel 2D materials such as Graphene Nanoribbon (GNR), holey carbon nitride C2N, and MXenes for energy storage, gas sensing, and spintronic applications, utilizing state-of-the-art techniques that combine Density Functional Theory (DFT) and Non-Equilibrium Greens Functions (NEGF) formalism; namely Vienna Ab-initio Simulation Package (VASP) and Atomistic Toolkit (ATK) package.Firstly, on the side of gas sensing, the burning of fossil fuels raises the level of toxic gas and contributes to global warming, necessitating the development of highly sensitive gas sensors. To start with, the adsorption and gas-sensing properties of bilaterally edge doped (B/N) GNRs were investigated. The transport properties revealed that the bilateral B/N edge-doping of GNR yielded Negative Differential Resistance (NDR) IV-characteristics, due to the electron back-scattering which was beneficial for selective gas sensing applications. Therefore, both GNR: B/N were found to be good sensors for NO2 and SO3 respectively. After that, the catalytic activity of four magnetic transition metal “TM” elements (e.g., Mn, Fe, Co and Ni) embedded in C2N pores, as Single-Atom Catalysts (SAC), was tested towards detecting toxic oxidizing gases. The results of spin-polarized transport properties revealed that Ni- and Fe-embedded C2N are the most efficient in detecting NO/ NO2 and NO2 molecules.Secondly, on the side of energy storage, since the fossil fuels reserves are depleting at an alarming rate, there is an urgent need for alternative forms of energy to meet the ever-growing demand for energy. Hydrogen is a popular form of clean energy. However, its storage and handling are challenging because of its explosive nature. The effect of magnetic moment on the hydrogen adsorption and gas-sensing properties in Mn-embedded in C2N were investigated. Two distinct configurations of embedment were considered: (i) SAC: 1Mn@C2N; and (ii) DAC: Mn2@C2N. Based on the huge changes in electronic and magnetic properties and the low recovery time (i.e., τ ≪ 1 s, τ = 92 μs and 1.8 ms, respectively), we concluded that C2N:Mn is an excellent candidate for (reusable) hydrogen magnetic gas sensor with high sensitivity and selectivity and rapid recovery time. Then, a comparative study of hydrogen storage capabilities on Metal- catalyst embedded (Ca versus Mn) C2N is presented which demonstrated the stability of these metal structures embedded on the C2N substrate. We proposed Ca@C2N and Mn@C2N for dual applications- hydrogen storage and a novel electrode for prospective metal-ion battery applications owing to its high irreversible uptake capacity 200 mAhg-1.Thirdly, on the side of data storage, spintronics is an emerging field for the next generation nanoelectronics devices to reduce their power consumption and to increase their memory and processing capabilities. Designing 2D-materials that exhibit half-metallic properties is important in spintronic devices that are used in low-power high-density logic circuits. We tested samples comprising of SAC and DAC of Mn embedded in a C2N sample size 2×2 primitive cells as well as their combinations in neighboring large pores. Many other TM catalysts were screened, and the results show the existence of half metallicity in just five cases: (a) C2N:Mn (DAC, SAC-SAC, and SAC-DAC); (b) C2N:Fe (DAC); and (c) C2N:Ni (SAC-DAC). Our results further showed the origins of half-metallicity to be attributed to both FMC and synergetic interactions between the catalysts with the six mirror images, formed by the periodic-boundary conditions.Lastly, on the side of batteries, sodium-sulfur batteries show great potential for storing large amounts of energy due to their ability to undergo a double electron- redox process, as well as the plentiful abundance of sodium and sulfur resources. However, the shuttle effect caused by intermediate sodium polysulfides (Na2Sn) limits their performance and lifespan. To address this issue, we proposed two functionalized MXenes Hf3C2T2 and Zr3C2T2 (T= F, O), as cathode additives to suppress the shuttle effect. We found that both Hf3C2T2 and Zr3C2T2 systems inhibit the shuttle effect by binding to Na2Sn with a binding energy higher than the electrolyte solvents. The decomposition barrier for Na2Sn on the O functionalized MXenes gets reduced which enhances the electrochemical process. Overall, our findings show that the tuning of 2D materials can lead to promising applications in various fields, including energy storage, gas sensing, and spintronics

    Semantic Debugging

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    Why does my program fail? We present a novel and general technique to automatically determine failure causes and conditions, using logical properties over input elements: "The program fails if and only if int(⟨length⟩) > len(⟨payload⟩) holds - that is, the given ⟨length⟩ is larger than the ⟨payload⟩ length." Our AVICENNA prototype uses modern techniques for inferring properties of passing and failing inputs and validating and refining hypotheses by having a constraint solver generate supporting test cases to obtain such diagnoses. As a result, AVICENNA produces crisp and expressive diagnoses even for complex failure conditions, considerably improving over the state of the art with diagnoses close to those of human experts

    Automated Testing of Software Upgrades for Android Systems

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    Apps’ pervasive role in our society motivates researchers to develop automated techniques ensuring dependability through testing. However, although App updates are frequent and software engineers would like to prioritize the testing of updated features, automated testing techniques verify entire Apps and thus waste resources. Further, most testing techniques can detect only crashing failures, necessitating visual inspection of outputs to detect functional failures, which is a costly task. Despite efforts to automatically derive oracles for functional failures, the effectiveness of existing approaches is limited. Therefore, instead of automating human tasks, it seems preferable to minimize what should be visually inspected by engineers. To address the problems above, in this dissertation, we propose approaches to maximize testing effectiveness while containing test execution time and human effort. First, we present ATUA (Automated Testing of Updates for Apps), a model-based approach that synthesizes App models with static analysis, integrates a dynamically refined state abstraction function, and combines complementary testing strategies, thus enabling ATUA to generate a small set of inputs that exercise only the code affected by updates. A large empirical evaluation conducted with 72 App versions belonging to nine popular Android Apps has shown that ATUA is more effective and less effort-intensive than state-of-the-art approaches when testing App updates. Second, we present CALM (Continuous Adaptation of Learned Models), an automated App testing approach that efficiently tests App updates by adapting App models learned when automatically testing previous App versions. CALM minimizes the number of App screens to be visualized by software testers while maximizing the percentage of updated methods and instructions exercised. Our empirical evaluation shows that CALM exercises a significantly higher proportion of updated methods and instructions than baselines for the same maximum number of App screens to be visually inspected. Further, in common update scenarios, where only a small fraction of methods are updated, CALM is even quicker to outperform all competing approaches more significantly. Finally, we minimize test oracle cost by defining strategies for selecting, for visual inspection, a subset of the App outputs. We assessed 26 strategies, relying on either code coverage or action effect, on Apps affected by functional faults confirmed by their developers. Our empirical evaluation has shown that our strategies have the potential to enable the identification of a large proportion of faults. By combining code coverage with action effect, it is possible to reduce oracle cost by about 41.2% while enabling engineers to detect all the faults exercised by test automation approaches

    2007 GREAT Day Program

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    SUNY Geneseo’s First Annual G.R.E.A.T. Day.https://knightscholar.geneseo.edu/program-2007/1001/thumbnail.jp

    2017 GREAT Day Program

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    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp
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